{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f1c72f79-5f18-43a8-a309-2c71b1f21745",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device cuda!\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from numpy.random import shuffle\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import wfdb\n",
    "from pathlib import Path\n",
    "import math\n",
    "from math import floor\n",
    "import cv2\n",
    "import pywt\n",
    "from functools import partial\n",
    "from collections import defaultdict\n",
    "from imblearn.over_sampling import SMOTE, ADASYN, RandomOverSampler\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "from numba import njit\n",
    "from tqdm import tqdm\n",
    "from time import time\n",
    "import tsfresh\n",
    "import tsfel\n",
    "import pickle as pkl\n",
    "import pandas as pd\n",
    "import traceback\n",
    "import sys\n",
    "import datetime\n",
    "import gc\n",
    "\n",
    "from ecgdetectors import Detectors\n",
    "\n",
    "import sklearn\n",
    "from sklearn.metrics import classification_report, confusion_matrix, f1_score, make_scorer, ConfusionMatrixDisplay\n",
    "from sklearn.metrics import balanced_accuracy_score, f1_score, precision_score, recall_score, accuracy_score\n",
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import RobustScaler, Normalizer, MinMaxScaler\n",
    "from sklearn.utils.class_weight import compute_class_weight\n",
    "\n",
    "import scipy\n",
    "from scipy import signal\n",
    "from scipy.stats import kendalltau\n",
    "\n",
    "import torch\n",
    "from torch.optim.lr_scheduler import StepLR\n",
    "from torch.backends import cudnn\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import TensorDataset, DataLoader\n",
    "from torch.utils.tensorboard import SummaryWriter\n",
    "from torch.utils.data.sampler import Sampler\n",
    "\n",
    "import skorch\n",
    "from skorch.helper import predefined_split\n",
    "from skorch.callbacks import EpochScoring, Initializer, LRScheduler, TensorBoard, Checkpoint, Initializer\n",
    "from skorch.dataset import Dataset\n",
    "\n",
    "cudnn.benchmark = False\n",
    "cudnn.deterministic = True\n",
    "\n",
    "DEVICE = 'cuda'\n",
    "\n",
    "print(f\"Using device {DEVICE}!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd52c8d3-7707-4624-8736-4646e5dae05e",
   "metadata": {},
   "source": [
    "# Data processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a530e645-e1ad-4e82-9a39-57c53f61c432",
   "metadata": {},
   "outputs": [],
   "source": [
    "@njit(parallel=True)\n",
    "def njit_dtw(s1, s2):\n",
    "    mat_d = np.ones((len(s1) + 1, len(s2) + 1)) * np.Inf\n",
    "    mat_d[0, 0] = 0\n",
    "    for i in range(1, len(s1) + 1):\n",
    "        for j in range(1, len(s2) + 1):\n",
    "            d = (s1[i - 1] - s2[j - 1])**2\n",
    "            mat_d[i, j] = d + min((mat_d[i-1, j], mat_d[i, j-1], mat_d[i-1, j-1]))\n",
    "    return np.sqrt(mat_d[len(s1), len(s2)])\n",
    "\n",
    "\n",
    "def read_ecg_data(path, record, lead, cwt_freqs=100, final_sampling_rate=None):\n",
    "    data = wfdb.rdsamp(str(path / str(record)))\n",
    "    annotations = wfdb.rdann(str(path / str(record)), extension='atr')\n",
    "    \n",
    "    sampling_rate = data[1]['fs']\n",
    "    try:\n",
    "        lead_id = data[1]['sig_name'].index(lead)\n",
    "    except ValueError:\n",
    "        return None\n",
    "    \n",
    "    X = data[0][:, lead_id]\n",
    "    kernel_1 = int(0.2 * sampling_rate)\n",
    "    if kernel_1 % 2 == 0:\n",
    "        kernel_1 -= 1\n",
    "    kernel_2 = int(0.6 * sampling_rate)\n",
    "    if kernel_2 % 2 == 0:\n",
    "        kernel_2 -= 1\n",
    "    baseline = signal.medfilt(signal.medfilt(X, kernel_1), kernel_2)\n",
    "    X -= baseline\n",
    "    \n",
    "    y = np.asarray(annotations.symbol)\n",
    "    loc = np.asarray(annotations.sample)\n",
    "\n",
    "    indices = [i for i, label in enumerate(y) if label in label_mapping.keys()]\n",
    "    y, loc = y[indices], loc[indices]\n",
    "    loc = np.clip(loc, 0, len(X) - 1)\n",
    "\n",
    "    # tol = 0.05\n",
    "    # new_locs = []\n",
    "    # for r in loc:\n",
    "    #     r_left = np.maximum(r - int(tol * sampling_rate), 0)\n",
    "    #     r_right = np.minimum(r + int(tol * sampling_rate), len(X))\n",
    "    #     new_locs.append(r_left + np.argmax(X[r_left:r_right]))\n",
    "    # loc = np.array(new_locs, dtype='int')\n",
    "    X = X / np.mean(X[loc])\n",
    "    if final_sampling_rate is not None and final_sampling_rate != sampling_rate:\n",
    "        sampling_ratio = final_sampling_rate / sampling_rate\n",
    "        time_vector = np.linspace(0, len(X) / sampling_rate, num=len(X))\n",
    "        new_time_vector = np.linspace(0, time_vector[-1], num=int(len(X) * sampling_ratio))\n",
    "        X = np.interp(new_time_vector, time_vector, X)\n",
    "        loc_resampled = np.round(loc * sampling_ratio).astype(int)\n",
    "        loc = np.clip(loc_resampled, 0, len(X) - 1)\n",
    "        sampling_rate = final_sampling_rate\n",
    "\n",
    "    y = np.asarray([label_mapping[l] for l in y])\n",
    "    \n",
    "    scales = pywt.central_frequency('mexh') * sampling_rate / np.arange(1, cwt_freqs + 1, 1)\n",
    "    X_cwt, _ = pywt.cwt(X, scales, 'mexh', 1 / sampling_rate)\n",
    "    return X, X_cwt, np.asarray(y), np.asarray(loc)\n",
    "\n",
    "\n",
    "def get_rr_data(loc):\n",
    "    loc = loc.astype(np.float32)\n",
    "    avg_rr = np.mean(np.diff(loc))\n",
    "    prev_rr = []\n",
    "    next_rr = []\n",
    "    ratio_rr = []\n",
    "    local_rr = []\n",
    "    for i in range(1, len(loc) - 1):\n",
    "            prev_rr.append(loc[i] - loc[i - 1] - avg_rr)\n",
    "            next_rr.append(loc[i + 1] - loc[i] - avg_rr)\n",
    "            ratio_rr.append((loc[i] - loc[i - 1]) / (loc[i + 1] - loc[i]))\n",
    "            local_rr.append(np.mean(np.diff(loc[np.maximum(i - 10, 0):i + 1])) - avg_rr)\n",
    "    return np.asarray(prev_rr), np.asarray(next_rr), np.asarray(local_rr), np.asarray(ratio_rr)\n",
    "\n",
    "\n",
    "def load_dataset_with_balancing(path, record_ids, lead='MLII', sampling_rate=360, n_before=90, n_after=110, cwt_freqs=100):\n",
    "    X_short_windows = []\n",
    "    X_cwt_windows = []\n",
    "    rr_windows = []\n",
    "    y_windows = []\n",
    "    for record in record_ids:\n",
    "        print(record)\n",
    "        # Read data\n",
    "        data = wfdb.rdsamp(str(path / str(record)))\n",
    "        annotations = wfdb.rdann(str(path / str(record)), extension='atr')\n",
    "        y = np.asarray(annotations.symbol)\n",
    "        loc = np.asarray(annotations.sample)\n",
    "        sampling_rate = data[1]['fs']\n",
    "        try:\n",
    "            lead_id = data[1]['sig_name'].index(lead)\n",
    "        except ValueError:\n",
    "            return None\n",
    "        X = data[0][:, lead_id]\n",
    "\n",
    "        # Remove baseline wander\n",
    "        kernel_1 = int(0.2 * sampling_rate)\n",
    "        if kernel_1 % 2 == 0:\n",
    "            kernel_1 -= 1\n",
    "        kernel_2 = int(0.6 * sampling_rate)\n",
    "        if kernel_2 % 2 == 0:\n",
    "            kernel_2 -= 1\n",
    "        baseline = signal.medfilt(signal.medfilt(X, kernel_1), kernel_2)\n",
    "        X -= baseline\n",
    "\n",
    "        # Only select relevant samples and map to their int label\n",
    "        indices = [i for i, label in enumerate(y) if label in label_mapping.keys()]\n",
    "        y, loc = y[indices], loc[indices]\n",
    "        y = np.asarray([label_mapping[l] for l in y])\n",
    "\n",
    "        # Normalize wrt average R peak amplitude\n",
    "        X = X / np.mean(X[loc])\n",
    "\n",
    "        # Calculate max window length\n",
    "        half_lengths = []\n",
    "        for i in range(10, len(loc) - 10):\n",
    "            current = loc[i]\n",
    "            half_lengths.append(current - loc[i - 10])\n",
    "            half_lengths.append(loc[i + 10] - current)\n",
    "        half_lengths = sorted(half_lengths)\n",
    "        max_half_length = half_lengths[int(len(half_lengths) * .99)]\n",
    "\n",
    "        # Get windows of 10 previous and next heartbeat\n",
    "        X_windows = []\n",
    "        for i in range(len(loc)):\n",
    "            if loc[i] - max_half_length > 0 and loc[i] + max_half_length <= len(X):\n",
    "                X_windows.append(X[loc[i] - max_half_length:loc[i] + max_half_length])\n",
    "        X_windows = np.asarray(X_windows)\n",
    "\n",
    "        # Calculate CWT of the longer windows\n",
    "        scales = pywt.central_frequency('mexh') * sampling_rate / np.arange(1, cwt_freqs + 1, 1)\n",
    "        X_cwt, _ = pywt.cwt(X_windows, scales, 'mexh', 1 / sampling_rate)\n",
    "        X_cwt = np.swapaxes(X_cwt, 0, 1)\n",
    "\n",
    "        # Generate single heartbeat windows\n",
    "        detectors = Detectors(sampling_rate)\n",
    "        for i in range(len(X_windows)):\n",
    "            # Find rpeak locations, current beat is second to last rpeak\n",
    "            detected_peaks = detectors.christov_detector(X_windows[i])\n",
    "            r_peak = min(detected_peaks, key=lambda x: abs(x - max_half_length))\n",
    "            r_peak_index = detected_peaks.index(r_peak)\n",
    "            if r_peak_index > len(detected_peaks) - 2 or r_peak_index < 1:\n",
    "                continue\n",
    "\n",
    "            prev_rr = detected_peaks[r_peak_index] - detected_peaks[r_peak_index - 1]\n",
    "            next_rr = detected_peaks[r_peak_index + 1] - detected_peaks[r_peak_index]\n",
    "            ratio_rr = prev_rr / next_rr\n",
    "            local_rr = np.mean(np.diff(detected_peaks[:r_peak_index]))\n",
    "            \n",
    "            cwt = X_cwt[i]\n",
    "            label = y[i]\n",
    "            cwt_window = cwt[:, r_peak - n_before:r_peak + n_after].T\n",
    "\n",
    "            X_short_windows.append(X_windows[i, r_peak - n_before:r_peak + n_after])\n",
    "            X_cwt_windows.append(cv2.resize(cwt_window, ((n_before + n_after) // 2, cwt_freqs)))\n",
    "            rr_windows.append((prev_rr, next_rr, local_rr, ratio_rr))\n",
    "            y_windows.append([label, record])\n",
    "\n",
    "    return np.asarray(X_short_windows), np.asarray(X_cwt_windows), np.asarray(rr_windows), np.asarray(y_windows)\n",
    "\n",
    "\n",
    "def load_dataset(path, record_ids, lead='MLII', sampling_rate=360, n_before=90, n_after=110, cwt_freqs=100):\n",
    "    X_windows = []\n",
    "    X_cwt_windows = []\n",
    "    rr_windows = []\n",
    "    y_windows = []\n",
    "\n",
    "    n_before = int(n_before * (sampling_rate / 360))\n",
    "    n_after = int(n_after * (sampling_rate / 360))\n",
    "    n_total = n_before + n_after\n",
    "    \n",
    "    for record in record_ids:\n",
    "        X, X_cwt, y, loc = read_ecg_data(path, record, lead, cwt_freqs=cwt_freqs, final_sampling_rate=sampling_rate)\n",
    "        prev_rr, next_rr, local_rr, ratio_rr = get_rr_data(loc)\n",
    "        y = y[1:-1]\n",
    "        loc = loc[1:-1]\n",
    "        \n",
    "        for i, (rpeak, label) in enumerate(zip(loc, y)):\n",
    "            window = X[rpeak - n_before:rpeak + n_after]\n",
    "\n",
    "            if len(window) == n_total:\n",
    "                X_windows.append(window)\n",
    "                X_cwt_windows.append(cv2.resize(X_cwt[:, rpeak - n_before:rpeak + n_after], (n_total // 2, cwt_freqs)))\n",
    "                rr_windows.append([prev_rr[i], next_rr[i], local_rr[i], ratio_rr[i]])\n",
    "                y_windows.append([label, record])\n",
    "            \n",
    "    return np.asarray(X_windows), np.asarray(X_cwt_windows), np.asarray(rr_windows), np.asarray(y_windows)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d424376f-398b-4d80-8ba5-ca4d4b917f35",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## MIT-BIH supraventricular processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "d96e807d-cd62-412b-ada9-1967046f272a",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_root = Path('data/mit-bih-supraventricular-arrhythmia-database-1.0.0/')\n",
    "\n",
    "label_mapping = {\n",
    "    \"N\": \"N\", \"L\": \"N\", \"R\": \"N\", \"e\": \"N\", \"j\": \"N\",  # N\n",
    "    \"A\": \"S\", \"a\": \"S\", \"S\": \"S\", \"J\": \"S\",  # SVEB\n",
    "    \"V\": \"V\", \"E\": \"V\",  # VEB\n",
    "    \"F\": \"F\",  # F\n",
    "    \"/\": \"Q\", \"f\": \"Q\", \"Q\": \"Q\"  # Q\n",
    "}\n",
    "\n",
    "label_categories = set(label_mapping.values())\n",
    "\n",
    "label_to_num = {k: float(i) for i, k in enumerate(label_categories)}\n",
    "num_to_label = {v: k for k, v in label_to_num.items()}\n",
    "sampling_rate = 128\n",
    "lead_id = 'ECG2'\n",
    "\n",
    "patient_ids = list(set([f.stem for f in data_root.glob('*') if f.stem.isnumeric()]))\n",
    "training_ids, testing_ids = train_test_split(patient_ids, train_size=.5, random_state=755)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "2aac1b3a-597a-4bc9-a62b-b58d1e4f0a26",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array(['N', 'V', '|', '~'], dtype='<U1'), array([1586,   85,    4,    4]))\n",
      "(array(['N', 'S', 'V', '|', '~'], dtype='<U1'), array([1667,   51,   44,   41,   33]))\n",
      "(array(['N', 'S'], dtype='<U1'), array([1844,   18]))\n",
      "(array(['N', 'Q', 'S', 'V', '|', '~'], dtype='<U1'), array([1102,    7, 1818,  235,  508,   31]))\n",
      "(array(['B', 'N', 'Q', 'S', 'V', '|', '~'], dtype='<U1'), array([   1, 2554,   11,  227,  555,    6,   91]))\n",
      "(array(['N', 'S', 'V', '~'], dtype='<U1'), array([1507,  632,   22,    2]))\n",
      "(array(['F', 'N', 'S', 'V', '|', '~'], dtype='<U1'), array([   1, 1846,   30,    6,   10,   28]))\n",
      "(array(['N', 'S', 'V', '|'], dtype='<U1'), array([1675,  658,    1,    3]))\n",
      "(array(['F', 'N', 'S', 'V'], dtype='<U1'), array([   1, 1946,    8,   97]))\n",
      "(array(['N', 'S', 'V', '|', '~'], dtype='<U1'), array([1466,  152,   91,    5,   24]))\n",
      "(array(['N', 'S', '|'], dtype='<U1'), array([2006,  135,    1]))\n",
      "(array(['N', 'Q', 'S', 'V', '|', '~'], dtype='<U1'), array([2485,    1,   53,  344,  125,   14]))\n",
      "(array(['N', 'S', 'V'], dtype='<U1'), array([2003,   74,   97]))\n",
      "(array(['N', 'Q', 'S', 'V', '|', '~'], dtype='<U1'), array([2035,    1,  227,  277,    6,   31]))\n",
      "(array(['N', 'S', 'V', '|', '~'], dtype='<U1'), array([2191,   68,   54,   21,   16]))\n",
      "(array(['N', 'S', 'V', '|', '~'], dtype='<U1'), array([3009,   69,  464,    1,   14]))\n",
      "(array(['N', 'V', '|', '~'], dtype='<U1'), array([2653,  181,    3,   51]))\n",
      "(array(['N', 'V', '|'], dtype='<U1'), array([2091,  207,  120]))\n",
      "(array(['F', 'N', 'S', 'V'], dtype='<U1'), array([   1, 1406,    1,   28]))\n",
      "(array(['N', 'S', 'V'], dtype='<U1'), array([2169,   26,   28]))\n",
      "(array(['N', 'S', 'V'], dtype='<U1'), array([2649,   21,   38]))\n",
      "(array(['N', 'Q', 'S', 'V', '|', '~'], dtype='<U1'), array([2561,    1,  322,  240,    1,    4]))\n",
      "(array(['N', 'S', 'V', '|', '~'], dtype='<U1'), array([2341,   44,    1,    3,    2]))\n",
      "(array(['N', 'S', 'V'], dtype='<U1'), array([1898,    9,   74]))\n",
      "(array(['N', 'S', 'V'], dtype='<U1'), array([2216,  196,    7]))\n",
      "(array(['N', 'S', 'V'], dtype='<U1'), array([2394,  104,   61]))\n",
      "(array(['N', 'S', 'V', '|'], dtype='<U1'), array([1746,   20,    8,    3]))\n",
      "(array(['F', 'N', 'V', '|', '~'], dtype='<U1'), array([   1, 1924,  143,    1,    2]))\n",
      "(array(['N', 'S', 'V', '|', '~'], dtype='<U1'), array([4228,   30,    1,    4,   26]))\n",
      "(array(['N', 'Q', 'S', 'V', '|'], dtype='<U1'), array([2107,   12,   48,   64,   14]))\n",
      "(array(['N', 'Q', 'S', 'V', '|', '~'], dtype='<U1'), array([1742,    1,  523,  577,    6,    8]))\n",
      "(array(['N', 'Q', 'S', 'V', 'a', '~'], dtype='<U1'), array([2482,    1,  613,   36,    1,    2]))\n",
      "(array(['N', 'S', 'V', '|', '~'], dtype='<U1'), array([1674,   17,  727,   25,   20]))\n",
      "(array(['F', 'N', 'S', 'V'], dtype='<U1'), array([   2, 2952,   40,   29]))\n",
      "(array(['N', 'S', 'V'], dtype='<U1'), array([1757,  150,    6]))\n",
      "(array(['N', 'S', 'V', '|', '~'], dtype='<U1'), array([1758,   14,   70,    9,   22]))\n",
      "(array(['N', 'Q', 'S', 'V', '|', '~'], dtype='<U1'), array([2354,    1,  125,  210,   22,  110]))\n",
      "(array(['N', 'S', 'V'], dtype='<U1'), array([2190,    9,   41]))\n",
      "(array(['N', 'Q', 'S', 'V'], dtype='<U1'), array([2828,    2,   27,   30]))\n",
      "*** Feature extraction started ***\n"
     ]
    },
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      "\n",
      "*** Feature extraction finished ***\n"
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   ],
   "source": [
    "X, X_cwt, rr_information, y = load_dataset(data_root, training_ids, lead=lead_id, sampling_rate=360)\n",
    "\n",
    "np.save(Path('processed_data/mit-supra/X_train'), X)\n",
    "np.save(Path('processed_data/mit-supra/X_cwt_train'), X_cwt)\n",
    "np.save(Path('processed_data/mit-supra/rr_train'), rr_information)\n",
    "np.save(Path('processed_data/mit-supra/y_train'), y)\n",
    "\n",
    "features_dict = tsfel.get_features_by_domain()\n",
    "X_feat = tsfel.time_series_features_extractor(features_dict, np.concatenate(X), fs=360, window_size=X.shape[-1])\n",
    "X_feat.to_csv(Path('processed_data/mit-supra/X_feat_train.csv'), index=False)\n",
    "\n",
    "X.shape, X_cwt.shape, X_feat.shape, rr_information.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "b3982617-b0ca-4934-a04a-578ed900e3cc",
   "metadata": {},
   "outputs": [
    {
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       "(94747, 234)"
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     "execution_count": 68,
     "metadata": {},
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   ],
   "source": [
    "X_feat = pd.read_csv(Path('processed_data/mit-supra/X_feat_train.csv'))\n",
    "y = np.load(Path('processed_data/mit-supra/y_train.npy'))[:, 0]\n",
    "\n",
    "tau_vals = sorted([(feat, (result.pvalue, -abs(result.statistic))) for feat, result in [(feat, kendalltau(X_feat[feat], y)) for feat in X_feat.columns]], key=lambda r: r[1])\n",
    "sorted_features = [feat for feat, _ in tau_vals]\n",
    "X_feat = X_feat[sorted_features].values\n",
    "sorted_features = np.asarray(sorted_features)\n",
    "\n",
    "np.save(Path('processed_data/mit-supra/X_feat_train'), X_feat)\n",
    "np.save(Path('processed_data/mit-supra/sorted_features'), sorted_features)\n",
    "X_feat.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "4634ecbd-69fc-4130-9660-40391a647ad2",
   "metadata": {},
   "outputs": [
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      "*** Feature extraction started ***\n"
     ]
    },
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   ],
   "source": [
    "X, X_cwt, rr_information, y = load_dataset(data_root, testing_ids, lead=lead_id, sampling_rate=360)\n",
    "\n",
    "np.save(Path('processed_data/mit-supra/X_test'), X)\n",
    "np.save(Path('processed_data/mit-supra/X_cwt_test'), X_cwt)\n",
    "np.save(Path('processed_data/mit-supra/rr_test'), rr_information)\n",
    "np.save(Path('processed_data/mit-supra/y_test'), y)\n",
    "\n",
    "features_dict = tsfel.get_features_by_domain()\n",
    "X_feat = tsfel.time_series_features_extractor(features_dict, np.concatenate(X), fs=360, window_size=X.shape[-1])\n",
    "X_feat.to_csv(Path('processed_data/mit-supra/X_feat_test.csv'), index=False)\n",
    "\n",
    "X.shape, X_cwt.shape, X_feat.shape, rr_information.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "7119f95d-4572-4222-b4f8-e6fcec9d941c",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_feat = pd.read_csv(Path('processed_data/mit-supra/X_feat_test.csv'))\n",
    "sorted_features = np.load(Path('processed_data/mit-supra/sorted_features.npy')).tolist()\n",
    "\n",
    "X_feat = X_feat[sorted_features].values\n",
    "\n",
    "np.save(Path('processed_data/mit-supra/X_feat_test'), X_feat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2e9c2de-17ca-462e-bcdc-3e801a4c77d9",
   "metadata": {},
   "source": [
    "## INCART processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "eb60efc5-40b1-43da-b97e-ab847d83e2f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_root = Path('data/st-petersburg-incart-12-lead-arrhythmia-database-1.0.0/files')\n",
    "\n",
    "patient_mapping = {\n",
    "    1: ['I01', 'I02'],\n",
    "    2: ['I03', 'I04', 'I05'],\n",
    "    3: ['I06', 'I07'],\n",
    "    4: ['I08'],\n",
    "    5: ['I09', 'I10', 'I11'],\n",
    "    6: ['I12', 'I13', 'I14'],\n",
    "    7: ['I15'],\n",
    "    8: ['I16', 'I17'],\n",
    "    9: ['I18', 'I19'],\n",
    "    10: ['I20', 'I21', 'I22'],\n",
    "    11: ['I23', 'I24'],\n",
    "    12: ['I25', 'I26'],\n",
    "    13: ['I27', 'I28'],\n",
    "    14: ['I29', 'I30', 'I31', 'I32'],\n",
    "    15: ['I33', 'I34'],\n",
    "    16: ['I35', 'I36', 'I37'],\n",
    "    17: ['I38', 'I39'],\n",
    "    18: ['I40', 'I41'],\n",
    "    19: ['I42', 'I43'],\n",
    "    20: ['I44', 'I45', 'I46'],\n",
    "    21: ['I47', 'I48'],\n",
    "    22: ['I49', 'I50'],\n",
    "    23: ['I51', 'I52', 'I53'],\n",
    "    24: ['I54', 'I55', 'I56'],\n",
    "    25: ['I57', 'I58'],\n",
    "    26: ['I59', 'I60', 'I61'],\n",
    "    27: ['I62', 'I63', 'I64'],\n",
    "    28: ['I65', 'I66', 'I67'],\n",
    "    29: ['I68', 'I69'],\n",
    "    30: ['I70', 'I71'],\n",
    "    31: ['I72', 'I73'],\n",
    "    32: ['I74', 'I75']\n",
    "}\n",
    "\n",
    "label_mapping = {\n",
    "    'N': 'N',\n",
    "    'V': 'V',\n",
    "    'A': 'S',\n",
    "    'j': 'N',\n",
    "    'F': 'F',\n",
    "    'S': 'S',\n",
    "    'n': 'S',\n",
    "    'B': 'N',\n",
    "    'R': 'N',\n",
    "    'Q': 'Q'\n",
    "}\n",
    "\n",
    "label_categories = set(label_mapping.values())\n",
    "\n",
    "label_to_num = {k: float(i) for i, k in enumerate(label_categories)}\n",
    "num_to_label = {v: k for k, v in label_to_num.items()}\n",
    "\n",
    "sampling_rate = 257\n",
    "lead_id = 'II'\n",
    "\n",
    "training_patients, testing_patients = train_test_split(list(patient_mapping.keys()), train_size=.5, random_state=755)\n",
    "training_ids = np.concatenate([[f for f in patient_mapping[p]] for p in training_patients]).tolist()\n",
    "testing_ids = np.concatenate([[f for f in patient_mapping[p]] for p in testing_patients]).tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f257dcd8-7d73-4569-b4e2-22eef27a97dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "*** Feature extraction started ***\n"
     ]
    },
    {
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   "source": [
    "X, X_cwt, rr_information, y = load_dataset(data_root, training_ids, lead=lead_id, sampling_rate=sampling_rate)\n",
    "\n",
    "np.save(Path('processed_data/incart/X_train'), X)\n",
    "np.save(Path('processed_data/incart/X_cwt_train'), X_cwt)\n",
    "np.save(Path('processed_data/incart/rr_train'), rr_information)\n",
    "np.save(Path('processed_data/incart/y_train'), y)\n",
    "\n",
    "features_dict = tsfel.get_features_by_domain()\n",
    "X_feat = tsfel.time_series_features_extractor(features_dict, np.concatenate(X), fs=360, window_size=X.shape[-1])\n",
    "X_feat.to_csv(Path('processed_data/incart/X_feat_train.csv'), index=False)\n",
    "\n",
    "X.shape, X_cwt.shape, X_feat.shape, rr_information.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ff674579-dd3a-4d46-99a3-ed49fdfa5ca1",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_feat = pd.read_csv(Path('processed_data/incart/X_feat_train.csv'))\n",
    "y = np.load(Path('processed_data/incart/y_train.npy'))[:, 0]\n",
    "\n",
    "tau_vals = sorted([(feat, (result.pvalue, -abs(result.statistic))) for feat, result in [(feat, kendalltau(X_feat[feat], y)) for feat in X_feat.columns]], key=lambda r: r[1])\n",
    "sorted_features = [feat for feat, _ in tau_vals]\n",
    "X_feat = X_feat[sorted_features].values\n",
    "sorted_features = np.asarray(sorted_features)\n",
    "\n",
    "np.save(Path('processed_data/incart/X_feat_train'), X_feat)\n",
    "np.save(Path('processed_data/incart/sorted_features'), sorted_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f54814d2-a693-4373-9660-0f0d67cbef78",
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      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, X_cwt, rr_information, y = load_dataset(data_root, testing_ids, lead=lead_id, sampling_rate=sampling_rate)\n",
    "\n",
    "np.save(Path('processed_data/incart/X_test'), X)\n",
    "np.save(Path('processed_data/incart/X_cwt_test'), X_cwt)\n",
    "np.save(Path('processed_data/incart/rr_test'), rr_information)\n",
    "np.save(Path('processed_data/incart/y_test'), y)\n",
    "\n",
    "features_dict = tsfel.get_features_by_domain()\n",
    "X_feat = tsfel.time_series_features_extractor(features_dict, np.concatenate(X), fs=360, window_size=X.shape[-1])\n",
    "X_feat.to_csv(Path('processed_data/incart/X_feat_test.csv'), index=False)\n",
    "\n",
    "X.shape, X_cwt.shape, X_feat.shape, rr_information.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9867a674-a606-4c1f-8d89-309933814166",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_feat = pd.read_csv(Path('processed_data/incart/X_feat_test.csv'))\n",
    "sorted_features = np.load(Path('processed_data/incart/sorted_features.npy')).tolist()\n",
    "\n",
    "X_feat = X_feat[sorted_features].values\n",
    "\n",
    "np.save(Path('processed_data/incart/X_feat_test'), X_feat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59986de4-19c8-46d4-8c97-7956cf8cd96e",
   "metadata": {},
   "source": [
    "## MIT BIH processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b2af0f60-1b76-4e0d-a587-ce3c57eb0a83",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_root = Path('/data/IDLab/DigiHealth/mit-bih-arrhythmia-database-1.0.0/')\n",
    "\n",
    "training_ids = [\n",
    "    101, 106, 108, 109, 112,\n",
    "    #114, 115, 116, 118, 119,\n",
    "    #122, 124, 201, 203, 205,\n",
    "    #207, 208, 209, 215, 220,\n",
    "    #223, 230\n",
    "]\n",
    "testing_ids = [\n",
    "    100, 103, 105, 111, 113,\n",
    "    117, 121, 123, 200, 202,\n",
    "    210, 212, 213, 214, 219,\n",
    "    221, 222, 228, 231, 232,\n",
    "    233, 234\n",
    "]\n",
    "\n",
    "label_mapping = {\n",
    "    \"N\": 0, \"L\": 0, \"R\": 0, \"e\": 0, \"j\": 0,  # N\n",
    "    \"A\": 1, \"a\": 1, \"S\": 1, \"J\": 1,  # SVEB\n",
    "    \"V\": 2, \"E\": 2,  # VEB\n",
    "    \"F\": 3,  # F\n",
    "    \"/\": 4, \"f\": 4, \"Q\": 4  # Q\n",
    "}\n",
    "\n",
    "sampling_rate = 360"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ff565a6f-ccd7-4f01-9da6-f5323f640390",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "101\n",
      "106\n",
      "108\n",
      "109\n",
      "112\n"
     ]
    }
   ],
   "source": [
    "X, X_cwt, rr_information, y = load_dataset_with_balancing(data_root, training_ids)\n",
    "np.save(Path('processed_data/mit-bih/X_train'), X)\n",
    "np.save(Path('processed_data/mit-bih/X_cwt_train'), X_cwt)\n",
    "np.save(Path('processed_data/mit-bih/rr_train'), rr_information)\n",
    "np.save(Path('processed_data/mit-bih/y_train'), y)\n",
    "\n",
    "if False:\n",
    "    features_dict = tsfel.get_features_by_domain()\n",
    "    X_feat = tsfel.time_series_features_extractor(features_dict, np.concatenate(X), fs=sampling_rate, window_size=X.shape[-1])\n",
    "    X_feat.to_csv(Path('processed_data/mit-bih/X_feat_train.csv'), index=False)\n",
    "    \n",
    "    X.shape, X_cwt.shape, rr_information.shape, X_feat.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f41c6657-821c-4ebf-a98d-1f7a719bbbb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_new = np.load(Path('processed_data/mit-bih/X_train.npy'))\n",
    "X_cwt_new = np.load(Path('processed_data/mit-bih/X_cwt_train.npy'))\n",
    "rr_new = np.load(Path('processed_data/mit-bih/rr_train.npy'))\n",
    "y_new = np.load(Path('processed_data/mit-bih/y_train.npy'))\n",
    "\n",
    "X_old = np.load(Path('processed_data/X_train.npy'))\n",
    "X_cwt_old = np.load(Path('processed_data/X_cwt_train.npy'))\n",
    "rr_old = np.load(Path('processed_data/rr_train.npy'))\n",
    "y_old = np.load(Path('processed_data/y_train.npy'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "54db38e9-825a-4ea7-8f21-78418da35e42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X_new[:100].T)\n",
    "plt.show()\n",
    "\n",
    "plt.plot(X_old[:100].T)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "94fe5ff7-044a-417a-92ba-30b2468cfff0",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_feat = pd.read_csv(Path('processed_data/mit-bih/X_feat_train.csv'))\n",
    "y = np.load(Path('processed_data/mit-bih/y_train.npy'))[:, 0]\n",
    "\n",
    "tau_vals = sorted([(feat, (result.pvalue, -abs(result.statistic))) for feat, result in [(feat, kendalltau(X_feat[feat], y)) for feat in X_feat.columns]], key=lambda r: r[1])\n",
    "sorted_features = [feat for feat, _ in tau_vals]\n",
    "X_feat = X_feat[sorted_features].values\n",
    "sorted_features = np.asarray(sorted_features)\n",
    "\n",
    "np.save(Path('processed_data/mit-bih/X_feat_train'), X_feat)\n",
    "np.save(Path('processed_data/mit-bih/sorted_features'), sorted_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "27ad92d8-68b3-40f7-bcde-350b2d71b9a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n",
      "103\n",
      "105\n",
      "111\n",
      "113\n",
      "117\n",
      "121\n",
      "123\n",
      "200\n",
      "202\n",
      "210\n",
      "212\n",
      "213\n",
      "214\n",
      "219\n",
      "221\n",
      "222\n",
      "228\n",
      "231\n",
      "232\n",
      "233\n",
      "234\n",
      "*** Feature extraction started ***\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "              <p>\n",
       "                  Progress: 100% Complete\n",
       "              <p/>\n",
       "              <progress\n",
       "                  value='49383'\n",
       "                  max='49383',\n",
       "                  style='width: 25%',\n",
       "              >\n",
       "                  49383\n",
       "              </progress>\n",
       "\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "*** Feature extraction finished ***\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "((49383, 200), (49383, 100, 100), (49383, 4), (49383, 234), (49383, 2))"
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     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X, X_cwt, rr_information, y = load_dataset_with_balancing(data_root, testing_ids)\n",
    "\n",
    "np.save(Path('processed_data/mit-bih/X_test'), X)\n",
    "np.save(Path('processed_data/mit-bih/X_cwt_test'), X_cwt)\n",
    "np.save(Path('processed_data/mit-bih/rr_test'), rr_information)\n",
    "np.save(Path('processed_data/mit-bih/y_test'), y)\n",
    "\n",
    "features_dict = tsfel.get_features_by_domain()\n",
    "X_feat = tsfel.time_series_features_extractor(features_dict, np.concatenate(X), fs=sampling_rate, window_size=X.shape[-1])\n",
    "X_feat.to_csv(Path('processed_data/mit-bih/X_feat_test.csv'), index=False)\n",
    "\n",
    "X.shape, X_cwt.shape, rr_information.shape, X_feat.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c0cf97f6-f24e-4f30-9b4b-01e4aef3e464",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_feat = pd.read_csv(Path('processed_data/mit-bih/X_feat_test.csv'))\n",
    "sorted_features = np.load(Path('processed_data/mit-bih/sorted_features.npy')).tolist()\n",
    "\n",
    "X_feat = X_feat[sorted_features].values\n",
    "\n",
    "np.save(Path('processed_data/mit-bih/X_feat_test'), X_feat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7eb6fb04-adbe-4179-b7be-4d6b333e6bba",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## Combine datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8915a704-290c-4273-a81e-7455f0f178ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "def resample_dataset(dataset_id, new_size, new_sampling_rate):\n",
    "    X_train = np.load(Path(f\"processed_data/{dataset_id}/X_train.npy\"))\n",
    "    rr_train = np.load(Path(f\"processed_data/{dataset_id}/rr_train.npy\"))\n",
    "    y_train = np.load(Path(f\"processed_data/{dataset_id}/y_train.npy\"))\n",
    "    \n",
    "    X_test = np.load(Path(f\"processed_data/{dataset_id}/X_test.npy\"))\n",
    "    rr_test = np.load(Path(f\"processed_data/{dataset_id}/rr_test.npy\"))\n",
    "    y_test = np.load(Path(f\"processed_data/{dataset_id}/y_test.npy\"))\n",
    "\n",
    "    print(X_train.shape)\n",
    "    X_train = scipy.signal.resample(X_train, new_size, axis=-1)\n",
    "    print(X_train.shape)\n",
    "    X_test = scipy.signal.resample(X_test, new_size, axis=-1)\n",
    "\n",
    "    new_dataset_id = dataset_id + f\"-{new_sampling_rate}Hz\"\n",
    "    dataset_path = Path('processed_data') / new_dataset_id\n",
    "    dataset_path.mkdir(exist_ok=True)\n",
    "    \n",
    "    np.save(dataset_path / 'X_train', X_train)\n",
    "    np.save(dataset_path / 'rr_train', rr_train)\n",
    "    np.save(dataset_path / 'y_train', y_train)\n",
    "    \n",
    "    np.save(dataset_path / 'X_test', X_test)\n",
    "    np.save(dataset_path / 'rr_test', rr_test)\n",
    "    np.save(dataset_path / 'y_test', y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4623914-261a-4d1c-9b3d-b88f6153def9",
   "metadata": {},
   "source": [
    "### Resample datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a3f3cadc-77f6-438d-a50e-7b6316e2f173",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(93714, 142)\n",
      "(93714, 71)\n"
     ]
    }
   ],
   "source": [
    "resample_dataset('incar', new_size=71, new_sampling_rate=128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9a662dc8-b54c-40f7-a37b-e56da60d44b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(52272, 200)\n",
      "(52272, 71)\n"
     ]
    }
   ],
   "source": [
    "resample_dataset('mit-bih', new_size=71, new_sampling_rate=128)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9a635a1-6a95-414a-a747-5628afd8bdf0",
   "metadata": {},
   "source": [
    "### Combine datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2e68bb25-13ef-40ba-8859-6d36979d9e01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((233983, 71),\n",
       " (224023, 71),\n",
       " (array(['N', 'S', 'V'], dtype='<U1'), array([207047,   6215,  20721])),\n",
       " (array(['N', 'S', 'V'], dtype='<U1'), array([196634,  10870,  16519])))"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasets = [Path('processed_data') / ds for ds in ['incar-128Hz', 'mit-bih-128Hz', 'mit-bih-supra']]\n",
    "\n",
    "label_mapping = {\n",
    "    'N': 'N',\n",
    "    'L': 'N',\n",
    "    'R': 'N',\n",
    "    'B': 'N',\n",
    "    'A': 'S',\n",
    "    'a': 'S',\n",
    "    'x': 'S',\n",
    "    'J': 'S',\n",
    "    'V': 'V',\n",
    "    #'F': 'F',\n",
    "    '!': 'V',\n",
    "    'e': 'N',\n",
    "    'j': 'N',\n",
    "    'E': 'V',\n",
    "    'S': 'S',\n",
    "    'n': 'S',\n",
    "    'J': 'S',\n",
    "}\n",
    "label_categories = set(label_mapping.values())\n",
    "label_to_num = {k: float(i) for i, k in enumerate(label_categories)}\n",
    "num_to_label = {v: k for k, v in label_to_num.items()}\n",
    "\n",
    "X_train = []\n",
    "rr_train = []\n",
    "y_train = []\n",
    "X_test = []\n",
    "rr_test = []\n",
    "y_test = []\n",
    "for ds_path in datasets:\n",
    "    X_train.append(np.load(ds_path / 'X_train.npy'))\n",
    "    rr_train.append(np.load(ds_path / 'rr_train.npy'))\n",
    "    y_train.append(np.load(ds_path / 'y_train.npy'))\n",
    "\n",
    "    X_test.append(np.load(ds_path / 'X_test.npy'))\n",
    "    rr_test.append(np.load(ds_path / 'rr_test.npy'))\n",
    "    y_test.append(np.load(ds_path / 'y_test.npy'))\n",
    "\n",
    "X_train = np.concatenate(X_train)\n",
    "rr_train = np.concatenate(rr_train)\n",
    "y_train = np.concatenate(y_train)\n",
    "\n",
    "X_test = np.concatenate(X_test)\n",
    "rr_test = np.concatenate(rr_test)\n",
    "y_test = np.concatenate(y_test)\n",
    "\n",
    "X_train = np.asarray([w for w, l in zip(X_train, y_train) if l[0] in label_mapping]).astype(np.float32)\n",
    "rr_train = np.asarray([w for w, l in zip(rr_train, y_train) if l[0] in label_mapping]).astype(np.float32)\n",
    "y_train = y_train[:, 0]\n",
    "y_train = np.asarray([label_mapping[l] for l in y_train if l in label_mapping])\n",
    "\n",
    "X_test = np.asarray([w for w, l in zip(X_test, y_test) if l[0] in label_mapping]).astype(np.float32)\n",
    "rr_test = np.asarray([w for w, l in zip(rr_test, y_test) if l[0] in label_mapping]).astype(np.float32)\n",
    "y_test = y_test[:, 0]\n",
    "y_test = np.asarray([label_mapping[l] for l in y_test if l in label_mapping])\n",
    "\n",
    "X_train.shape, X_test.shape, np.unique(y_train, return_counts=True), np.unique(y_test, return_counts=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0c76ddef-b75a-4a11-ab20-10b0fc3a9084",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save(Path('processed_data/combined/X_train'), X_train)\n",
    "np.save(Path('processed_data/combined/rr_train'), rr_train)\n",
    "np.save(Path('processed_data/combined/y_train'), y_train)\n",
    "\n",
    "np.save(Path('processed_data/combined/X_test'), X_test)\n",
    "np.save(Path('processed_data/combined/rr_test'), rr_test)\n",
    "np.save(Path('processed_data/combined/y_test'), y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "032d2246-e41f-4b57-b732-5e8c9b278e99",
   "metadata": {},
   "source": [
    "# Supervised learning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edf9d521-e395-4bf1-b770-d16d950a8ba9",
   "metadata": {},
   "source": [
    "## Baseset load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2b993422-61e6-452b-9ba6-fa85ecd95beb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((50251, 100, 100), (48989, 100, 100))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_id = 'mit-bih'\n",
    "\n",
    "allowed_labels = [0, 2, 1]\n",
    "num_to_label = {0: 'N', 1: 'S', 2: 'V', 3: 'F', 4: 'Q'}\n",
    "label_to_num = {v: k for k, v in num_to_label.items()}\n",
    "\n",
    "#X_train = np.load(Path(f\"processed_data/{dataset_id}/X_train.npy\"))\n",
    "X_cwt_train = np.load(Path(f\"processed_data/{dataset_id}/X_cwt_train.npy\")).astype(np.float32)\n",
    "X_feat_train = np.load(Path(f\"processed_data/{dataset_id}/X_feat_train.npy\")).astype(np.float32)\n",
    "rr_train = np.load(Path(f\"processed_data/{dataset_id}/rr_train.npy\")).astype(np.float32)\n",
    "y_train = np.load(Path(f\"processed_data/{dataset_id}/y_train.npy\"))[:, 0].astype(np.int64)\n",
    "\n",
    "#X_test = np.load(Path(f\"processed_data/{dataset_id}/X_test.npy\"))\n",
    "X_cwt_test = np.load(Path(f\"processed_data/{dataset_id}/X_cwt_test.npy\")).astype(np.float32)\n",
    "X_feat_test = np.load(Path(f\"processed_data/{dataset_id}/X_feat_test.npy\")).astype(np.float32)\n",
    "rr_test = np.load(Path(f\"processed_data/{dataset_id}/rr_test.npy\")).astype(np.float32)\n",
    "y_test = np.load(Path(f\"processed_data/{dataset_id}/y_test.npy\"))[:, 0].astype(np.int64)\n",
    "\n",
    "#X_train = np.asarray([w for w, l in zip(X_train, y_train) if l[0] in allowed_labels]).astype(np.float32)\n",
    "X_cwt_train_filtered = np.asarray([w for w, l in zip(X_cwt_train, y_train) if l in allowed_labels])\n",
    "X_feat_train_filtered = np.asarray([w for w, l in zip(X_feat_train, y_train) if l in allowed_labels])\n",
    "rr_train_filtered = np.asarray([w for w, l in zip(rr_train, y_train) if l in allowed_labels])\n",
    "y_train_filtered = np.asarray([l for l in y_train if l in allowed_labels]).astype(np.int64)\n",
    "\n",
    "del X_cwt_train, X_feat_train, rr_train, y_train\n",
    "gc.collect()\n",
    "X_cwt_train = X_cwt_train_filtered\n",
    "X_feat_train = X_feat_train_filtered\n",
    "rr_train = rr_train_filtered\n",
    "y_train = y_train_filtered\n",
    "\n",
    "#X_test = np.asarray([w for w, l in zip(X_test, y_test) if l[0] in allowed_labels]).astype(np.float32)\n",
    "X_cwt_test_filtered = np.asarray([w for w, l in zip(X_cwt_test, y_test) if l in allowed_labels])\n",
    "X_feat_test_filtered = np.asarray([w for w, l in zip(X_feat_test, y_test) if l in allowed_labels])\n",
    "rr_test_filtered = np.asarray([w for w, l in zip(rr_test, y_test) if l in allowed_labels])\n",
    "y_test_filtered = np.asarray([l for l in y_test if l in allowed_labels]).astype(np.int64)\n",
    "\n",
    "del X_cwt_test, X_feat_test, rr_test, y_test\n",
    "gc.collect()\n",
    "X_cwt_test = X_cwt_test_filtered\n",
    "X_feat_test = X_feat_test_filtered\n",
    "rr_test = rr_test_filtered\n",
    "y_test = y_test_filtered\n",
    "\n",
    "# ecg_scaler = MinMaxScaler()\n",
    "# X_train = ecg_scaler.fit_transform(X_train)\n",
    "# X_test = ecg_scaler.transform(X_test)\n",
    "\n",
    "rr_scaler = RobustScaler()\n",
    "rr_train = rr_scaler.fit_transform(rr_train)\n",
    "rr_test = rr_scaler.transform(rr_test)\n",
    "\n",
    "feat_scaler = RobustScaler()\n",
    "X_feat_train = feat_scaler.fit_transform(X_feat_train)\n",
    "X_feat_test = feat_scaler.transform(X_feat_test)\n",
    "\n",
    "X_cwt_train.shape, X_cwt_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18a9d0e2-cd1f-4320-915a-6ff30f285965",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## Full load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "eb106e64-44ee-4f0a-a727-a54de47cf067",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_id = 'combined'\n",
    "\n",
    "label_to_num = {'N': 0, 'S': 1, 'V': 2}\n",
    "num_to_label = {v: k for k, v in label_to_num.items()}\n",
    "\n",
    "X_train = np.load(Path(f\"processed_data/{dataset_id}/X_train.npy\"))\n",
    "rr_train = np.load(Path(f\"processed_data/{dataset_id}/rr_train.npy\"))\n",
    "y_train = np.load(Path(f\"processed_data/{dataset_id}/y_train.npy\"))\n",
    "y_train = np.asarray([label_to_num[l] for l in y_train]).astype(np.int64)\n",
    "\n",
    "X_test = np.load(Path(f\"processed_data/{dataset_id}/X_test.npy\"))\n",
    "rr_test = np.load(Path(f\"processed_data/{dataset_id}/rr_test.npy\"))\n",
    "y_test = np.load(Path(f\"processed_data/{dataset_id}/y_test.npy\"))\n",
    "y_test = np.asarray([label_to_num[l] for l in y_test]).astype(np.int64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f4472aaf-a865-4783-b135-ec30f1dadc60",
   "metadata": {},
   "outputs": [],
   "source": [
    "sampler = RandomUnderSampler(random_state=7665)\n",
    "X_train, _ = sampler.fit_resample(X_train, y_train)\n",
    "rr_train, y_train = sampler.fit_resample(rr_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ac164a9-8c33-4055-88b2-955eba62b969",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## Data exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "9107a6ad-7ce8-4ddf-b440-8d8377fe4596",
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "too many indices for array: array is 1-dimensional, but 2 were indexed",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_2342/2342927971.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_counts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'F'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_counts\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mtest_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_counts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'F'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_counts\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_labels\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_labels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtrain_counts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_counts\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: too many indices for array: array is 1-dimensional, but 2 were indexed"
     ]
    }
   ],
   "source": [
    "train_labels, train_counts = np.unique(y_train[y_train[:, 0] == 'F'][:, 1], return_counts=True)\n",
    "test_labels, test_counts = np.unique(y_test[y_test[:, 0] == 'F'][:, 1], return_counts=True)\n",
    "\n",
    "plt.figure(figsize=(20, 5))\n",
    "plt.bar(np.arange(len(train_labels) + len(test_labels)), np.concatenate([train_counts, test_counts]))\n",
    "plt.xticks(np.arange(len(train_labels) + len(test_labels)), np.concatenate([train_labels, test_labels]))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "1ff409d4-fd2c-45d8-8df5-464656712e8b",
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "too many indices for array: array is 1-dimensional, but 2 were indexed",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_2342/339340707.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtraining_beats\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'F'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'208'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mtesting_beats\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'F'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'213'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mncols\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msharex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msharey\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: too many indices for array: array is 1-dimensional, but 2 were indexed"
     ]
    }
   ],
   "source": [
    "training_beats = X_train[(y_train[:, 0] == 'F') & (y_train[:, 1] == '208')]\n",
    "testing_beats = X_test[(y_test[:, 0] == 'F') & (y_test[:, 1] == '213')]\n",
    "\n",
    "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(20, 10), sharex=True, sharey=True)\n",
    "\n",
    "axs[0].plot(training_beats.T, color='b', alpha=.01)\n",
    "axs[0].set_title('Patient 208')\n",
    "axs[0].set_xticks([])\n",
    "axs[0].set_yticks([])\n",
    "axs[1].plot(testing_beats.T, color='orange', alpha=.01)\n",
    "axs[1].set_title('Patient 213')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "cd10242d-7ac8-4840-a4b3-473941a9aa2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "training_local_beats = X_local_train[(y_train[:, 0] == 'F') & (y_train[:, 1] == '208')]\n",
    "testing_local_beats = X_local_test[(y_test[:, 0] == 'F') & (y_test[:, 1] == '213')]\n",
    "\n",
    "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(20, 10), sharex=True, sharey=True)\n",
    "\n",
    "axs[0].plot(training_local_beats.T, color='b', alpha=.01)\n",
    "axs[0].set_title('Patient 208')\n",
    "axs[0].set_xticks([])\n",
    "axs[0].set_yticks([])\n",
    "axs[1].plot(testing_local_beats.T, color='orange', alpha=.01)\n",
    "axs[1].set_title('Patient 213')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "8072a5eb-019f-47b0-8ccc-a8bd2c4ff76d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VjzKtrbkQmba66ka12iRmX0O3WVmRzpxxYfrxx92211+Pe1/TPWv9x7xxg5E/nLRI/68SxphXrLUXyt6vS5/DANC00Gdxb0eQ807T1zmy28a62lCJRMzEptB+7+4ejSrv7rqv5H29fFk6ezb83maN7ksucBOOkaXs/yv/d/7sWffV9pInAMD89DYgZ4W/tLySi2nrbttWV5tXIhEzsWna/d7dDR+AtPEgAu1W5v9V+nc+fQDX1pInAMD89DYgp8NfSFYoK1t364fpvb2Tk4marKudtRY75kAjJPQ8bTuIQPuVaeMWOkORoJUgAKC3AVk6Pqt9dTX7NlmhrEyozBqtMsbV07ah3VTeaG3MgUDWKPPKSvzzZ9WNZoWd5WV3cMFpcGQp08Yt5kwEZysAoN96HZB9ZUagypQAZIXpBw9cPW0b2k3ljdbGHgik22fduBE/qmzMybA7GklXrkiDwdFtkoMLToMjJLaNW8yZCM5WAEC/EZAnYkegxmM3ipkl649q2+tp8w4Mpt13/72U8ktYrM3uq3zrlnRwcHSbd945fhtOg1djUXpyV+nSpfzfSVoJAgAIyJ5kBOqFF9z3Tz99PDQkJQdJcPOF/qi2vZ4278Bgln0fjY7Cd1EnwXTgLqoRDd0P5SxST+6qJAdf6d/JRx9tR8kTAKAdCMgpeaEhFNwGg/Af1TKlG00JnZqedd9jg246cMcG37YcZCyqphbLaVLod/Ls2XaUPAEA2oGAnJIXGkLB7fAw/Ec1PUK7siI98sjJ0ek2KjPxKUtM0M0K3DHBt20HGYuoifKfpks62l7yBABoBwJySt4f0GlLDvzSjbffXqzJZkVlJ3lC78tgkB+4Q10s2tL5oyvmXf7ThpKOtpc8AQtteyy9tCa9uOQut1v8xw0oQEBOyfsDWkfJwSKc0p422ITer1u38k9nZ41cP/+8dP8+p8GrNO/ynzb8/i9CyROwkLbH0p11aX9HknWXd9YJyVhYBOSUvD+gdZUctOH0bt6p75hgk3X/0PslFZ9mj23ZhenN+vtcVtO///48gqSFIGcjgIq8uiEdpP5QHOy77cACMraoxYDnwoUL9u7duzXuTjskf0iTsookHM9qbS17YYzBwAXBKp+rjGSE2A/Bw+FRcFhayu5EYYzb76L7l3kudFfo93911R0E1amNv3fGmFestRfK3q8vn8NYMC8uScrKE0Z66nDeewNEC30W1z6C3PSknGn2o67Ry9CyzAcHzdYkF40QF9Vt5t0//b5fv978aXY0o8nyhjaUdwCdNgz8oQhtB1qu1oDchkk5bdqP9Cnt5DSvr4k/2kWnvouCTej+OzvS5cvH3/fd3XL7UKV0WL92rR0Hb30x75IOX9PlHUDnPbkpDVJ/KAZDtx1YQLUG5LaM2rRlP6Tjo9OHgbNO8/6jXTRCXBRsqugAkPcYVZyFyDpIevbZ5g+a+qap2nK6VwA1Oz+SLm5Jw1VJxl1e3HLbgQVUa0Buy6hNW/YjrS1/tGNOfYeCzXgs7e3N9vx5p9mrGv2PWbSEU+7dRfcKYA7Oj6SPvuZqjj/6GuEYC63WgNyWANiW/Uhryx/taU99J+E1VDYRsuT91q2s5D9XVaP/sQdDTR80oR5NlncAABZPrQG5LQGwLfuRVvaPdp0THqc59Z239HYev7Rkd9dN3Au9lqLR/9j3JPZgqOmDJtSH1oFAARb6AP5QrQG5LaM2bdmP0L7F/NFuaqJhXgANhdeDg+xuHSG7u+HXkjf6X+Y9CXUQ8bXhoAkAGsFCH8Axtbd5a8uozbT70ZY2dU1MNCwKoKHw6i8IEiv0WvJG/8u8J1kHSVevtvOgCQDmjoU+gGNYKCRHmxYXKFqsow5FCzsUvT+h+4eEXkto4ZYm3hOgKiwUglYJLvQh6UO3mXCHzmpsoZBFVseo7TQj0uPx8YltvjprZovqf4tKV2LKGnxJ2UTWktVZo/9tnXwJAAsnb0EPSi3QQ70JyNME06rbw01TR5zc5+Dg5HV118zGBNCs8Jq8108/LT3yiOtU4Zc1rKycfMzhULp0qdz7c+mSe9z041BHPD9tKUECMKOshT4SlFqgh3oRkKed4Fb1COU0I9J5nSLqLvWYpvtH+r3e3ZXeflt64QUXoG/elO7fl27fdoFZcq9lf9+9ntj3ZzyWbt06XmJhjHTlSjNdQPqoLStUAqhAstBHyD49MNEvvQjI05ZKVN0ebpoR6bxOEU8/XW/Qm6b7R+x7PRodvb/J6HjWKLnkgld6eejLl08+j7XSyy9nPwZhrnptWqESQAXOjyYr4WXIK8EAOqgXAXnaUom8gFh2NHLaOuK86+YR9JIge+6ce782NvKfq8x7HbO6XSK9PHRI6DrCXPXaukIlgBlklVoMhm470CO9CMihkGltcbgN1diWGY2cpY44ZqJbnUGv7GstU5ZSR5BKFilJH8CEgjNhbnpMkgQ6KCm1GK5KMu4yKb1gERH0SOcD8ngs7e2Fr9/ZkZ55ptwIbOxoZBLSssoBpLg64vQodkhdQa/syGuZspRQkBoM8l9rnoOD7FAfejzC3PTaukIlgBmdH0kffU166tBdSiwigt7pdEBOgtLubv7tHjxwyx3Hijm17Ie0kMPDuEl2ySj2Cy+El3GuK+iVPY1epm45FLBu3XLvzWqgFC7P6mp2qLeWjhdVa/MKlQA8sy4hHVpE5G6JP5zAgul0QC5T41oUon0xp5ZjnrtMqM0r01heri/oTXMaPXbVwqr7KEuuQ0booMRawlzV2rJSJoCAaZaQTgfq/cCH6sNdRpHRWZ0OyHWVHcScWi567rzRy6wJgHmBe9pyhBhlTqNP00YtL2D5ATrWW2+Fr0tWACTMdR8t/YCJMktIb4+lz5yVPnf5eKBWzh8Z+iOjozodkMuM0GYtXhESc2o577nzRi9Dk+LySjUePKhvkl7safS62qglAXqacgsf5RT9QUs/wBPqX7y/c3yU+M41N7L8IOt0amAJ6rzHBxZcpwNy1ujn6dMn260tL0s3bpR77KJTy6GR19u380cvQ5PiQrXHiTq7McScRq+ijdq1a9KpUy6Inzrlvk9cujTNnjuUU/QLLf3Qa+nyiOXHw7f1R4m/8MmTI80x6I+Mjup0QM4a/XzuOennfu74tuefrz48ZT33lSvuj3Tead+8hUHy6nGb7sYwa0/ca9dcj2N/0ZBnnz0KyaEFQIokZRWE4/6gPzN6K6ve+OGXIu+cM0osKTMu0B8ZHdbpgCxlj37mjYhWWbvoP8/mpuvOUHTaNxR0k1HQrFKQNpQPPB4YpAi9nuR9TkaLn302+3Zbk/ab04abnR1qUPuG/szorax6Yx1W9ODpx1mSzl9xLeGADup8QC4jq3bx6addiJs1ZMWe9s2bFDcauS4Nt2+3qxvDeCx95Ssnt58+HZ7M59dVh5aY9q+bJdzMWoPKhK/FQn9m9E5SVhHqNlGLQ2n7Fl0s0FnG2qLTKkcuXLhg7969W+PuNCtvxbXEyoqrVy4bSJeWXOhOM8aNMPuSrhX37rlgmITjtgq9bysrLtDH3j7LYCC9885RqI5t25clKbcoI+t5h8PmD0qQbxH+DxljXrHWXih7v65/DqOkpKximvrhKgxXjxYTARZQ6LOYEWRPzGn83d3pRiPLnPZdtN6yofct6S0du+xzlvV1dzlNy7fY/czDhK/FtGj/h4CpZZZVzBFdLNBRvQvIeafLY0/jTxOQ6jzt23QJQOh9M8ZNsotd9jnt3e92k/OSOuXLl+vZzzxM+ALQak0HVLpYoKN6FZCL+qOWWbmt7OSvZATUn2T3yCOldj9TG3q+bm5mh15r3WuOWfY57fRp1985XadcJmBn7WdZTPgC0GpNBlS6WKDDehWQi06Xlz2NP00Yffvto39PW67ha0MJwGiUXV8thSfgWXuyH3ViMHC9qdO12f59y1pZme40e+igaW+PyXoAGtTIxDzPcFW6uEUXC3RWrwJymdPlsaOUZcJoHWE27zXNs/QidFCRt8BJKAAfHuYvGV3WcHhyIZjY9yZr5F+q5uAGAKZyrN9xA5646ibmEY7RYb0KyEWny9PlCrFi61HrqGcNvabHH6+u9CImTIZqrNfX48tWErOULwyH0tWr+W3wypaljEbSmTMntzNZD8DcbY+lz19pbmLe0rulizebeW5gjjoVkIuCXFaIM+aonvj69enaiMUGujrqWUPBVKpmtDo2TGatHLi1Jd28ebTYR4xk4mLWgigxkufM62AwzUg+k/UANC4ZObY5zePrdvjV5p4bmKPOBOSYIJeuMTbmaKR4Z+eoLVkWY1xoO3365PZLl+L2sY5OFqFg+vrr2bcvG+iKwqR/ULKx4V5LOpiORuESjJWV7NHeGzdOvtdFrl51l2fOuMczxpV4JMtVJ2LCbvpgq+xKgQBQuaZbugE90pmAHDsqmPRHXV2NL6NYXXWh7/596Qd+4Hh9srVuCemY0oVQmJ21R2tWz9eqRquLapxjSxVCBwc3bmSP9o5G0nPPHb1XRc6ckT78YenKleP1y4eHbhlrPyQXhd2s1/Xmm27iYHr/WZ0NwNw03dJNkpanPL0HLJjOBOSyp8BjR1LTIejll08G6zKlC/NawKCq0eq8oF2mVGGagwP/vcrrLDIcSp/8pHveUNeMpMxjPJa+9KWT1w8GR+9N1ut68EB617vatcQ3gJ5pQ8/hCzeKbwN0QGcCctkR09D20Cn/RJ21qFV2nahqtDovaJd9L0IHB7GTANMjuIlkhDnvZ5AE5+vXs7tn+ME69Dh7e9klJAAwF09uut7DTVleoXMFeqMzAbnsiGnZU/6JuhaOqHrBj/HYjYTeu+f2bXNzukCXF7SreC/KTAJ87LHsx3jrLemZZ8KlE9JRu7m8OvMrV9zz5u0/XSsANOb8yPUebupPN6PH6JHOBOSyI6bTjrBWPdEuGT29fLm6HslVh+3QyG8V70WZMo28cPvggRvhDdUrf9d3Fe/LwYH0/d9/tHpfltDoctPLfQPoifMjaVDBMqxlnZ7D6HGy+MmLS+5ymw9SNKczAVkqX9+bvr1UHHKqnGjnB9mQaUo35rW6Xsx7URQcY8o0rl0Lr7rn+9rXpEcfzb7us5+Vzp4tnvAXWrwkkTW63IblvgH0wPZY+sxZ6aDClZRifbCG0WM/EH/mrPT5j00WP7Hu8s46IRmNMbbEihgXLlywd+/erXF3mpOEHD9YDof1TsRaW8sPx5ILnUl4j7W0lN2hw5jiAFilmPc09B4kr/vaNdeFog1Cvw9Fr8FXVekLFp8x5hVr7YWy9+vy5zByJD2QG2nztiQ9VXHv5TvXpC98UlJBBhmuulX7gJqEPos7NYI8i3mNuvqKRoenLd0I1dAuLc13VDOmh/Le3sn7+a+7zCIjdco7UxA7WZGRZgBTu3u9wR7IFY6sJKPgX3hWheFYakdrO/QSAXmiiZXS8iaDzVK6kVUbLLka23kGspgeyum64pWV46871LZtngaD/JKd2MmKTRyEAeiA7bH0MGcSRt2GOX02y0hGwR+UeC1taG2HXiIgT1TVnaLMZK3QJLfbt2drI5bUBiedG3zzDGRleyhLbsGP5HWnV8Bryvp6/vWxkxVZrhrAVF5t+Cj6yYhTmTET7MquBGiW454bqEEvA3JWiK2iI0PZU+h1rayX1LmGRl/TSyonk9eMcf+uaoR5lh7KTdYeJxMCBwO3fPXNm0fXhQ6AHvEmladHwRN1tQgE0HFNlhnE9D5ORoaLJtjtF0y6SbMtOIWI3updQA6FWGn2sDrNKfSqV9aL6YzhL6n8zDPHyxx2d6WPfayakDwaud7CyUj2YOC+j+mhXGXtcRJ4V1bcV5HBwI3iv/POyXCc/t155hn3fvnv4dtvH3+8JFTv7JzsosFy1QAKNVVmMBjG9T7OGhk+2D8+8n1nmlOCh3SyQGN6F5DzQuysYbVoVHQevXJDpQsJP5BtbLj+wWkPH7oV52bd1/FYunXraCT74MAtCW2Mm5yXXhnP37e82uOrV+PaviUOD91jf+AD0htvFN/+4cPsg5rQEtQPHx7flp6I6B+wWHsUklmuGkCh7bH0tfvzf14zcIuSxPQ+Do1w+9t/c8pRj4N96XPfT0jG3PUuINdZB5o3Klq2/GLaMJ33OtKBLO+2u7uzd1vICpRJ+7ndXRcUV1aOj9hL7vXm+dSnyrer2993vZBjJ/2lR+DH4+KWfL7kvQ29B0kLOMIxgKDtsfRrzzTT9/jbb8UvDBIa4fa3z1QucSh97vKUo9DAdHoXkOusA82ruS1TfjFLO7DQ68gKZGVe8zST+4oOOh48cJPy/IVaispDkvvNQ/J+Jz+PMqzN73PNxDwAhV7dkA7n9IHne+JquVXzntx05Ri+pdPSw72jSXtFvvG73ah1ni98kpFkzM3CBuRpR1irXira50+6k1wtaxIsY4JSFctOl3l9m5vS6dPFj5m1rzFiArj/mEXlIfN2/bq7nHa/smqOE0zMA1Coicl5H7otXbxZfDvf+ZErxxiuSjJuWerDg0lrOhs3OW/3c9L7ikYibPMdPdAbCxmQZxlhratzhP/4SUhNTufHBKWqlp0u8/pGI+m5545PXMubyFY21IX6Mfsef/zoQKdMCcM87O66n8sso71+zXGCiXkAosx7cp4ZlBs59p0fuRXvnkrq30qWVBzsS7/9suuakWfeBw0x7evQSQsZkK9fn23Bhao7R6SF6k7zglLMKGVsQC3z+kYj6f59t3/Wun/fuJEdbPf2ytch++3Psrz55tGBTht9/OOzj/YmNcd1HJAB6LB59wAuHMGNVGYhEN/+Pdc1Yynn1GbdBw1+IP7MWenzHytuX4dOWriAPB6fXH0tMc1IXx2dJUL7kReU6lp2ehrJKHR6JHl3N36kPrRSXtq86omntbcnXbp08oDh9OnshViyJPXfdR2QAeiYJKR97vL8nvOJq8WlFXWPpg7PuZHob3tOGjx68vrBsN6DhnQ/5we7kk21KUq3r0NnLVxAzhslLjPSlyyQcfny7N0aYvcjLyjVtez0tEYjN4EuLXakvm31xLP49KdPHjC85z3Su99dfF/KKQCUciykzVFMOI5ZDEQqLpPI4off8yPpr++5euikrnm4Gt92blqxK/01uXAL5mbhAnLeSOvOTtwocN7oZhVLMYcmyl26FB6trmvZ6VnM0hIvZkS8TC/jtNXVuEU/qrC7K/3qrx5fAGR3V3orp/MS5RQAplJ2OeYqDFeLbxOzGIjkAnNgzk1QqOeyX9f80dfqDcdSfPBtauEWzNXCBeSiUeKYUeCi0c1ZW3BlTZS7csUtmhEara578uA0ZmmJFzMiXraXsX//zU3pq1+d7v7T2NqKHxFfXaWcAsCU5j06aZbjyhZiFgNJRpnL1iCX6blcp9OPF9+m7jIPtMbCBeSYzghFo8BFAbiKFlzpiXIvv1w8sbDuyYNlzdISL2ZEfDVn0CLU9UM6OrjIG8GtWuwCI5RUAJhJqdHJskO1Kcsr0rc/HxdOYxYDmWb0+/RKO8Lx9lh6+JX828yjzAOtsXABOT3SGpIXgvMCcEzAmWZiX50r+NW1hHXeqLb/nGfPui//+dM9oY1xBwSXL7vbjsfhg52VFdc9IvTzTfpLt8HKSrtG/QEsuCc387s4JAZD6YmPq3RIHgxdbe9TVvpr9+PDXtZiIOnR1NJ100b64I2S95lB3iTDVzdOTshL27/nbkcXi15YuIAsHR9pDY1C5oXgvGBWFHCm7cH8eODMzayj1bP0hI6RNaqdfs7dXfeVfv6kJ/Ty8vE2bru7ruREOhnAv/u7pTfekJ59Nrv1m99fet6yRsRv3GjXqD+ABXd+JA3ek3+bpGb34s1yIXmWEdD0YiDpx5pqGWg7v9HYokmGUeGeVm99spAB2TdNGUDWyOjt264HcFHAKbNkdGI8dv1+05aXj/Zz2lHgafZnVkU13P7zb2xIDzMOyg8O3GjyxoZ7Dw4P3STGz342PwDv78e3V6vSYNC+GnEAHfXw9ZwrB8drdi/elD70wvFV7Mxy6i6TUeNZJ7qFJs1tj90y0JlMuKtFzOTAqhRNMixa5jp0P3RWowG5itKAaSe3xdT7Zu3fNKUSGxvZ/X4fPnTXXbs2/ShwnaUbZZ8z6zZFt/Vf69ZW3PM3MYJ8cNC+GnEAHbWcM1nsQxkT2vzg+r33XV3xvNujKbTak5Xe+wGdGOWe92S3okmGtuQfFlq9dZ6xJZYwu3Dhgr17924lT5ycpvdHIofD9ozKhfbvkUey28MlPY6zLC3lrxRnTPb1eY+ZWFvLXqI55r7TCj1n1vPH3Da5fduWmvYZI73wQjt+N9ENxphXrLUXyt6vys9htND2WPq1Z6TD1KiKWY6fUDdvLy4pHJADlh6VDvfdJL8nN+t/XS+tZZdRDFfdwUXo+pDkflh4oc/ixkaQmygNKCO0f1L5ko6iOuNQeI4ZqZ2l08S0ijqJ+M8fux/37jVTOpEW6q1sbXt+NwF02KsbJ8OxJC0/1s5wLE3XF/jwLQVreutYsa9okmHo+ieuFk9ORCc1FpCbKA3IEirzCO3H66+XL+mIaU2XJWYCXxP9k9PPubLivrKefzSSrl4tfsxz59yIfVOSFnT374dvU/S7WVc3EQA9Ejp1/yCvLrliZQNqVrgsw6/pLbNiXxlFkwxD11+8mX8/dFZjJRZNlAak5ZV5bGxUu3/jcfgxpZNlFm0qN6lC3utfXpaef9691mvX3Os+OHDvyaOPSnt71e/P1auuN/W9ey6cb24evdfT/G62vWQI7UOJBTIVlQLULQmo/oS2wbA4FG6PXcideols42qom379iT98PffmVwaCRrSuxKKJ0oC0vDKPqvcvmeB1+3b24378493ukOC//tOpFp9+v+ObN6V33nEHC4eHrvtHXr/raRgjffjDRxPuNjfdzzwZ+b10qfzPvu0lQwAWREy/4TrFLimdlkwU/NDt6UaTkzKNmBX76lbXKDYWSmMBuY7SgKLFK9LyyjzqKl0IPe7Nm9N11Vg0WR09HjzIDpLJ6y1xkiOKX0+c1Uf61i3Xp7nMz74tJUMAFlxRKUDdZg2oWfv/xNXiNnTJAUDMin11m/YgAZ3SWIlF1bJOcfuyTne3ocwjVldO4Yc6ehjjDg4SRT/PWSXPV/Z3ICkVSZdmLNLvEtqBEgu00jxKHPLKF6Yt8ahSsCvHpAwEndK6EouqlVm8ItGGMo9YXTmFH5p4mN5+/Xr451lFyUXyfGVGfvNWLVyk3yUACJpHiUdowZHkujpH0GMmILZhFBuN60xALrN4RaLuMo8qyyC6cgo/JkiOx9m9phOzllwkzzceh8N2VpDPO0hpopsIgI6qo81ZrKZLPJJ9CAXoWcTWFjddB45W6EyJRZnFK+pSZxlEl07hh8oUErGLi0xjMHA/ow9/WHrmmewVDv2uGr7Y8hAgBiUWyNSGEoOuKlM+QheL3gh9FncmIE9Tg1y1OkNsV2qQY1TdtSJtedm1kQuF2pWV7H7IXTpIQfMIyMjUljZnXURtMTJ0vga5zOIVs8groaizDIJT+NV5+DB/xPf1QD9+6owB1K4Nbc4WhV+K8pmz0r85S20xKtOZgCwd9do9PHQjgPfv57dNKytvkpYUPwEt5nmyQrj/+qp6TW2SvO6mWZtdP85BCoDaEeLipOuJH+xKD3eVWVt855r086eyR+aXTlNbjEydCsh1K+okUcUIY1EI7yr/dbdB6H3v+kEKgIYxQSxbeuLi3esnexX7kr7Fd65JX3hWsgfZt6u60T46ozM1yPMQM0mraAJakb7WudY5MW8WXX/f0RxqkBHEBLHjsiYuRjGSWQqH4wT13b3W+RrkeYgpoZh1hLEr7dzKauvry9uvMi39urAKIoA5qavN2aLKWtkuxvBccTiWqO9GJgJyCfOYpFVVHXMV5hnqmnh9MUL7VaYUpq9lMwBQiWkCbFKWYgbFt6W+GxnmGpDbMoo27X7MY5JWWzolxIS6Kn+eWa87xiDw2be0FG4XF9tGLu99L7OyYVdWQQSARoQC7OmVowVNTq9Iyys6sbjJ+9bzH5v6boRYa6O/PvjBD9pp3b5t7XBorYtb7ms4dNvnqS37kef2bWtXV601xl02sW+rq8ffo+RrdfVoH6t+H/3XvbJi7fJy9j74zxW6Xgrv49Wr7vHz7ruykv9ajMm+nzGz3Rb9IemuLfH5m3zN8jkMLKQv3rb2F4bWjnX09QtDtz3Gr1219sXB5L5L1v78o9aOjbX/fjX+MdBZoc/iuX0wFwWueWnLfrRdUaibx/uYDswrKycPGgaD7P0YDE4+RvpgI7kuuX2y/zEhv8zr53cOWQjIQAlfvO0CbZlgO8190Duhz+K5dbFoyzK9bdmPtivqptGW9zGvXKLEr3ZpZVY27NMqiIhHFwugRizZjUiNd7Foy+SztuxH2xXVQrflfVxdzd4+GExXGx1bV12mHp0FRgBgzrI6XyS9kYEIcwvIbZl81pb9aLuiUNeW9zE0ue/goHzHiLLdJsq09GOBEQCYI5bsxozmFpDbMorWlv1YBHmhru73cdqR3KyuFrEdI+roNtGWzi0A0Css2Y0ZsZIeWmeWmt1ZaqOrrqum9hh5qEEGakQNMiI1XoMMxIoZyQ2NzM5SG111XTX9jwGgIedHLgwnfZL93shAhFNN7wCQVrTcdnpkNqkVllxNctaobUxt9Cz3zdvf2O0AgAqdHxGIMTVGkNE6RSO5eSOzs9RGV11X3ZZOHwAAoBwCMuauaOJaUYeMopHZWTpGVNltoi2dPgAAQDkE5Bx0IKheTCu1opHcRRmZpWMKAACLiS4WAXQgqEfRCn0x+NmgC+hiAQDNo4tFSXQgqEcVE9cYmQUAAHWii0UAHQjqce5c9ghy2fKI0YhADAAA6sEIcsCi1LkuGiauAQCAtiMgByxKkFu0iYRdLY9YtJ8DAAAIo8QiIAlsGxuurOLcOReO2xTk8hbMaNN+pnWtPGJRfw4AsFC2x9KrG9L+PWl4Tnpyk4VAUBu6WCywKjpCYHb8HDANulgAJWyPpTvr0oE3e34wZPlozIwuFh3ERMJ24OcAADV7deN4OJbc96/SWgr1ICAvMCYStgM/BwCo2X5gxCG0HZgRAXmBLcpEwqbVPYGOnwMA1GwYGHEIbQdmREBeYF3tCFGlmKWtZ8XPAQBq9uSmqzn2DYZuO1ADJumh05hAh7Zikh5QEl0sUIPQZzFt3tBpTKADgI44PyIQY24osUCnMYEOAACURUBGpy3CBDpW4QMAoF0IyOi0tk+gm8ckQgAAUA4BGZ03GrkJeYeH7rIt4VhyS5nvp3rf7++77QAAoBkEZKBBTCIEAKB9CMhAg5hECABA+xCQgQYtwiRCAAD6hoAMNKjtkwgBAOgjFgoBGjYaEYgBAGgTRpABAAAADwEZAAAA8BCQAQAAAA8BGQAAAPAQkAEAAAAPARkAAADw9DYgj8fS2pq0tOQux+Om9wgAAABt0MuAPB5L6+vSzo5krbtcX682JBPAAQAAFlMvA/LGhrS/f3zb/r7bXoV5BHAAAADUo5cB+d69ctvLqjuAAwAAoD69DMjnzpXbXlbdARwAAAD16WVA3tyUhsPj24ZDt70KdQdwAAAA1KeXAXk0kra2pNVVyRh3ubXltleh7gAOAACA+pxqegeaMhpVF4izHltyNcf37rmR483N+p4PAAAA1eltQK5bnQEcAAAA9elliQXQFvTLBgCgfRhBBhqS9MtOWgIm/bIlzj4AANAkRpCBhtAvGwCAdiIgAw2hXzYAAO1EQAYaQr9sAADaiYAMNIR+2QAAtBMBGWhI3QvWAACA6dDFAmgQ/bIBAGgfRpABAAAADwEZAAAA8BCQAQAAAA8BGQAAAPAQkAEAAAAPARkAAADwEJABAAAADwEZAAAA8BCQAQAAAI+x1sbf2Jg/kLRT3+4AQG+sWmu/oeyd+BwGgEplfhaXCsgAAABA11FiAQAAAHgIyAAAAICHgAwAAAB4CMgAAACAh4AMAAAAeAjIAAAAgIeADAAAAHgIyAAAAICHgAwAAAB4/j99KQHgkznD/AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "training_rr = rr_train[(y_train[:, 0] == 'F') & (y_train[:, 1] == '208')]\n",
    "testing_rr = rr_test[(y_test[:, 0] == 'F') & (y_test[:, 1] == '213')]\n",
    "\n",
    "pca = PCA(n_components=2)\n",
    "training_pca = pca.fit_transform(training_rr)\n",
    "\n",
    "pca = PCA(n_components=2)\n",
    "testing_pca = pca.fit_transform(testing_rr)\n",
    "\n",
    "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(10, 5), sharex=True, sharey=True)\n",
    "\n",
    "axs[0].scatter(training_pca[:, 0], training_pca[:, 1], color='b')\n",
    "axs[0].set_title('Patient 208')\n",
    "axs[0].set_xticks([])\n",
    "axs[0].set_yticks([])\n",
    "axs[1].scatter(testing_pca[:, 0], testing_pca[:, 1], color='orange')\n",
    "axs[1].set_title('Patient 213')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7ad8f47-3ef7-4f59-9d03-3e0047244f8f",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## Resampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "efbdbdb3-93ee-411a-9aab-b842297418f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "sm = SMOTE(random_state=123)\n",
    "X_train_resampled, y_train_resampled = sm.fit_resample(X_train, y_train)\n",
    "sm = SMOTE(random_state=123)\n",
    "X_local_train_resampled, _ = sm.fit_resample(X_local_train, y_train)\n",
    "sm = SMOTE(random_state=123)\n",
    "rr_train_resampled, _ = sm.fit_resample(rr_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e5002b04-490c-4fba-9632-a44ca72e2258",
   "metadata": {},
   "outputs": [],
   "source": [
    "training_patients = np.unique(y_train[:, 1])\n",
    "X_train_resampled = []\n",
    "X_local_train_resampled = []\n",
    "rr_train_resampled = []\n",
    "y_train_resampled = []\n",
    "for patient_id in training_patients:\n",
    "    indices = y_train[:, 1] == patient_id\n",
    "    X, X_local, rr, y = X_train[indices], X_local_train[indices], rr_train[indices], y_train[indices, 0]\n",
    "    \n",
    "    if np.unique(y).shape[0] > 1:\n",
    "        resampler = RandomOverSampler(random_state=123)\n",
    "        X, _ = resampler.fit_resample(X, y)\n",
    "        resampler = RandomOverSampler(random_state=123)\n",
    "        X_local, _ = resampler.fit_resample(X_local, y)\n",
    "        resampler = RandomOverSampler(random_state=123)\n",
    "        rr, y = resampler.fit_resample(rr, y)\n",
    "        \n",
    "        X_train_resampled.append(X)\n",
    "        X_local_train_resampled.append(X_local)\n",
    "        rr_train_resampled.append(rr)\n",
    "        y_train_resampled.append(y)\n",
    "        \n",
    "X_train_resampled = np.concatenate(X_train_resampled)\n",
    "X_local_train_resampled = np.concatenate(X_local_train_resampled)\n",
    "rr_train_resampled = np.concatenate(rr_train_resampled)\n",
    "y_train_resampled = np.concatenate(y_train_resampled).astype(np.float64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d0be2d93-bded-4f52-af4a-e12514c234c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "std = .00\n",
    "\n",
    "X_train = X_train_resampled + std * np.random.randn(*X_train_resampled.shape).astype(np.float32)\n",
    "X_local_train = X_local_train_resampled + std * np.random.randn(*X_local_train_resampled.shape).astype(np.float32)\n",
    "rr_train = rr_train_resampled + std * np.random.randn(*rr_train_resampled.shape).astype(np.float32)\n",
    "y_train = y_train_resampled.astype(np.int64)\n",
    "#y_test = y_test[:, 0].astype(np.float64).astype(np.int64)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13e7a8af-e07f-404c-820a-207a9df23f8a",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "63aa8cee-a080-4626-91aa-155d2ae28d3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "class CNN2D(torch.nn.Module):\n",
    "    def __init__weights(w):\n",
    "        if isinstance(w, torch.nn.Linear) or isinstance(w, torch.nn.Conv2d):\n",
    "            torch.nn.init.kaiming_normal_(w.weight)\n",
    "            torch.nn.init.constant_(w.bias, val=0.0)\n",
    "    \n",
    "    def __init__(self, n_features=0, n_lt_features=0, lrelu_slope=.2):\n",
    "        super(CNN2D, self).__init__()\n",
    "        self.n_classes = len(allowed_labels)\n",
    "        self.lrelu_slope = lrelu_slope\n",
    "        self.n_features = n_features\n",
    "        self.n_lt_features = n_lt_features\n",
    "        self.feature_head_output = 64 if (n_features != 0 or n_lt_features != 0) else 4\n",
    "        self.beat_head = torch.nn.Sequential(\n",
    "            torch.nn.Conv2d(in_channels=1, out_channels=16, kernel_size=7),\n",
    "            torch.nn.BatchNorm2d(16),\n",
    "            torch.nn.LeakyReLU(self.lrelu_slope),\n",
    "            torch.nn.MaxPool2d(5),\n",
    "            \n",
    "            torch.nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3),\n",
    "            torch.nn.BatchNorm2d(32),\n",
    "            torch.nn.LeakyReLU(self.lrelu_slope),\n",
    "            torch.nn.MaxPool2d(3),\n",
    "            \n",
    "            torch.nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3),\n",
    "            torch.nn.BatchNorm2d(64),\n",
    "            torch.nn.LeakyReLU(self.lrelu_slope),\n",
    "            torch.nn.AdaptiveMaxPool2d((1, 1)),\n",
    "            \n",
    "            torch.nn.Flatten()\n",
    "        ).to(DEVICE)\n",
    "\n",
    "        self.feature_head = torch.nn.Sequential(\n",
    "            torch.nn.Linear(4 + self.n_features + self.n_lt_features, 128),\n",
    "            torch.nn.LeakyReLU(self.lrelu_slope),\n",
    "            torch.nn.Linear(128, 128),\n",
    "            torch.nn.LeakyReLU(self.lrelu_slope),\n",
    "            torch.nn.Linear(128, self.feature_head_output),\n",
    "            torch.nn.LeakyReLU(self.lrelu_slope),\n",
    "        ).to(DEVICE)\n",
    "        \n",
    "        self.linear_blocks = torch.nn.Sequential(\n",
    "            torch.nn.Linear(64 + self.feature_head_output, 256),\n",
    "            torch.nn.LeakyReLU(self.lrelu_slope),\n",
    "            torch.nn.Linear(256, 64),\n",
    "            torch.nn.LeakyReLU(self.lrelu_slope),\n",
    "            torch.nn.Linear(64, 32),\n",
    "            torch.nn.LeakyReLU(self.lrelu_slope),\n",
    "            torch.nn.Linear(32, self.n_classes)\n",
    "        ).to(DEVICE)\n",
    "        \n",
    "        self.beat_head.apply(CNN2D.__init__weights)\n",
    "        self.feature_head.apply(CNN2D.__init__weights)\n",
    "        self.linear_blocks.apply(CNN2D.__init__weights)\n",
    "\n",
    "\n",
    "    def forward(self, X, X_rr, X_feat=None, X_lt_feat=None):\n",
    "        X = torch.unsqueeze(X, 1)\n",
    "        if X_feat is not None:\n",
    "            X_feat = torch.cat((X_rr, X_feat), dim=1)\n",
    "        if X_lt_feat is not None:\n",
    "            if X_feat is not None:\n",
    "                X_feat = torch.cat((X_feat, X_lt_feat), dim=1)\n",
    "            else:\n",
    "                X_feat = torch.cat((X_rr, X_lt_feat), dim=1)\n",
    "        \n",
    "        z_beat = self.beat_head(X)\n",
    "        if X_feat is not None:\n",
    "            z_feat = self.feature_head(X_feat)\n",
    "        else:\n",
    "            z_feat = X_rr\n",
    "        \n",
    "        z = torch.cat((z_beat, z_feat), dim=1)\n",
    "\n",
    "        return self.linear_blocks(z)\n",
    "\n",
    "\n",
    "class CNNDeep2D(torch.nn.Module):\n",
    "    def __init__weights(w):\n",
    "        if isinstance(w, torch.nn.Linear) or isinstance(w, torch.nn.Conv2d):\n",
    "            torch.nn.init.kaiming_normal_(w.weight)\n",
    "            torch.nn.init.constant_(w.bias, val=0.0)\n",
    "\n",
    "    def __init__(self, n_features=0):\n",
    "        super(CNNDeep2D, self).__init__()\n",
    "        self.n_classes = len(allowed_labels)\n",
    "        self.n_features = n_features\n",
    "        self.feature_head_output = 32 if n_features != 0 else 4\n",
    "\n",
    "        self.conv_block_1 = torch.nn.Sequential(\n",
    "            torch.nn.Conv2d(in_channels=5, out_channels=64, kernel_size=7, padding='same'),\n",
    "            torch.nn.BatchNorm2d(64),\n",
    "            torch.nn.ReLU(),\n",
    "        ).to(DEVICE)\n",
    "        self.conv_block_1.apply(CNNDeep2D.__init__weights)\n",
    "\n",
    "        self.conv_block_2 = torch.nn.Sequential(\n",
    "            torch.nn.Conv2d(in_channels=64, out_channels=64, kernel_size=7, padding='same'),\n",
    "            torch.nn.BatchNorm2d(64),\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.Conv2d(in_channels=64, out_channels=64, kernel_size=7, padding='same'),\n",
    "            torch.nn.BatchNorm2d(64),\n",
    "        ).to(DEVICE)\n",
    "        self.conv_block_2.apply(CNNDeep2D.__init__weights)\n",
    "\n",
    "        self.conv_block_3 = torch.nn.Sequential(\n",
    "            torch.nn.MaxPool2d(4),\n",
    "            torch.nn.Conv2d(in_channels=64, out_channels=128, kernel_size=5, padding='same'),\n",
    "            torch.nn.BatchNorm2d(128),\n",
    "            torch.nn.ReLU(),\n",
    "        ).to(DEVICE)\n",
    "        self.conv_block_3.apply(CNNDeep2D.__init__weights)\n",
    "\n",
    "        self.conv_block_4 = torch.nn.Sequential(\n",
    "            torch.nn.Conv2d(in_channels=128, out_channels=128, kernel_size=5, padding='same'),\n",
    "            torch.nn.BatchNorm2d(128),\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.Conv2d(in_channels=128, out_channels=128, kernel_size=5, padding='same'),\n",
    "            torch.nn.BatchNorm2d(128),\n",
    "        ).to(DEVICE)\n",
    "        self.conv_block_4.apply(CNNDeep2D.__init__weights)\n",
    "\n",
    "        self.conv_block_5 = torch.nn.Sequential(\n",
    "            torch.nn.MaxPool2d(5),\n",
    "            torch.nn.Conv2d(in_channels=128, out_channels=256, kernel_size=5, padding='same'),\n",
    "            torch.nn.BatchNorm2d(256),\n",
    "            torch.nn.ReLU(),\n",
    "        ).to(DEVICE)\n",
    "        self.conv_block_5.apply(CNNDeep2D.__init__weights)\n",
    "\n",
    "        self.conv_block_6 = torch.nn.Sequential(\n",
    "            torch.nn.Conv2d(in_channels=256, out_channels=256, kernel_size=5, padding='same'),\n",
    "            torch.nn.BatchNorm2d(256),\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.Conv2d(in_channels=256, out_channels=256, kernel_size=5, padding='same'),\n",
    "            torch.nn.BatchNorm2d(256),\n",
    "        ).to(DEVICE)\n",
    "        self.conv_block_6.apply(CNNDeep2D.__init__weights)\n",
    "\n",
    "        self.flatten = torch.nn.Sequential(\n",
    "            torch.nn.AdaptiveMaxPool2d((1, 1)),\n",
    "            torch.nn.Flatten()\n",
    "        )\n",
    "\n",
    "        self.feature_head = torch.nn.Sequential(\n",
    "            torch.nn.Linear(4 + self.n_features, 64),\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.Linear(64, 64),\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.Linear(64, 32),\n",
    "            torch.nn.ReLU(),\n",
    "        ).to(DEVICE)\n",
    "        self.feature_head.apply(CNNDeep2D.__init__weights)\n",
    "        \n",
    "        self.linear_blocks = torch.nn.Sequential(\n",
    "            torch.nn.Linear(256 + self.feature_head_output, 256),\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.Linear(256, 32),\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.Linear(32, self.n_classes)\n",
    "        ).to(DEVICE)\n",
    "        self.linear_blocks.apply(CNNDeep2D.__init__weights)\n",
    "\n",
    "    \n",
    "    def forward(self, X, X_rr, X_feat=None):\n",
    "        if X_feat is not None:\n",
    "            X_feat = torch.cat((X_rr, X_feat), dim=1)\n",
    "        \n",
    "        z_1 = self.conv_block_1(X)\n",
    "        z_2 = F.relu(z_1 + self.conv_block_2(z_1))\n",
    "        z_3 = self.conv_block_3(z_2)\n",
    "        z_4 = F.relu(z_3 + self.conv_block_4(z_3))\n",
    "        z_5 = self.conv_block_5(z_4)\n",
    "        z_6 = F.relu(z_5 + self.conv_block_6(z_5))\n",
    "        z_beat = self.flatten(z_6)\n",
    "        \n",
    "        if X_feat is not None:\n",
    "            z_feat = self.feature_head(X_feat)\n",
    "        else:\n",
    "            z_feat = X_rr\n",
    "        \n",
    "        z = torch.cat((z_beat, z_feat), dim=1)\n",
    "\n",
    "        return self.linear_blocks(z)\n",
    "\n",
    "\n",
    "class CNN1D(torch.nn.Module):\n",
    "\n",
    "    def __init__weights(w):\n",
    "        if isinstance(w, torch.nn.Linear) or isinstance(w, torch.nn.Conv2d):\n",
    "            torch.nn.init.kaiming_normal_(w.weight)\n",
    "            torch.nn.init.constant_(w.bias, val=0.0)\n",
    "    \n",
    "    def __init__(self, n_features=0):\n",
    "        super(CNN1D, self).__init__()\n",
    "        self.n_classes = len(allowed_labels)\n",
    "        self.n_features = n_features\n",
    "        self.beat_head = torch.nn.Sequential(\n",
    "            torch.nn.Conv1d(in_channels=1, out_channels=16, kernel_size=7),\n",
    "            torch.nn.BatchNorm1d(16),\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.MaxPool1d(5),\n",
    "            \n",
    "            torch.nn.Conv1d(in_channels=16, out_channels=32, kernel_size=3),\n",
    "            torch.nn.BatchNorm1d(32),\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.MaxPool1d(3),\n",
    "            \n",
    "            torch.nn.Conv1d(in_channels=32, out_channels=64, kernel_size=3),\n",
    "            torch.nn.BatchNorm1d(64),\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.AdaptiveMaxPool1d(1),\n",
    "            \n",
    "            torch.nn.Flatten()\n",
    "        ).to(DEVICE)\n",
    "\n",
    "        self.feature_block = torch.nn.Sequential(\n",
    "            torch.nn.Linear(4 + self.n_features, 64),\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.Linear(64, 16),\n",
    "            torch.nn.ReLU()\n",
    "        ).to(DEVICE)\n",
    "\n",
    "        self.beat_head.apply(CNN1D.__init__weights)\n",
    "        self.linear_blocks.apply(CNN1D.__init__weights)\n",
    "        \n",
    "        \n",
    "    def forward(self, X, X_rr, X_feat=None):\n",
    "        X = torch.unsqueeze(X, 1)\n",
    "        \n",
    "        z_beat = self.beat_head(X)\n",
    "        \n",
    "        z = torch.cat((z_beat, X_rr), dim=1)\n",
    "        if X_feat is not None:\n",
    "            z = torch.cat((z, X_feat), dim=1)\n",
    "\n",
    "        return self.linear_blocks(z)\n",
    "\n",
    "\n",
    "class FocalLoss(torch.nn.Module):\n",
    "    def __init__(self, alpha=None, gamma=2., reduction='mean'):\n",
    "        super(FocalLoss, self).__init__()\n",
    "        self.gamma = gamma\n",
    "        self.alpha = alpha\n",
    "        self.reduction = reduction\n",
    "\n",
    "    def forward(self, inputs, targets):\n",
    "        # inputs => [batch_size, num_classes], targets => [batch_size]\n",
    "        \n",
    "        log_prob = F.log_softmax(inputs, dim=1)\n",
    "        prob = torch.exp(log_prob)\n",
    "\n",
    "        # Focal Loss formula\n",
    "        loss = -1 * (1 - prob) ** self.gamma * log_prob\n",
    "\n",
    "        # Apply class weights\n",
    "        if self.alpha is not None:\n",
    "            alpha = self.alpha[targets]\n",
    "            loss = loss * alpha.unsqueeze(1)\n",
    "\n",
    "        loss = torch.gather(loss, 1, targets.unsqueeze(1))\n",
    "\n",
    "        if self.reduction == 'mean':\n",
    "            loss = loss.mean()\n",
    "        elif self.reduction == 'sum':\n",
    "            loss = loss.sum()\n",
    "\n",
    "        return loss\n",
    "\n",
    "\n",
    "class MatrixWeightedCrossEntropy(torch.nn.Module):\n",
    "    def __init__(self, weight_matrix, reduction='mean'):\n",
    "        super(MatrixWeightedCrossEntropy, self).__init__()\n",
    "        self.weight_matrix = weight_matrix\n",
    "        self.reduction = reduction\n",
    "\n",
    "\n",
    "    def forward(self, inputs, targets):\n",
    "        loss = F.cross_entropy(inputs, targets, reduction='none')\n",
    "        weights = self.weight_matrix[targets, torch.argmax(inputs, dim=1)]\n",
    "        loss = loss * weights\n",
    "    \n",
    "        if self.reduction == 'mean':\n",
    "            loss = loss.mean()\n",
    "        elif self.reduction == 'sum':\n",
    "            loss = loss.sum()\n",
    "\n",
    "        return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8fdf0e74-941b-477c-9c0e-860d48f5b405",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_and_evaluate_model(X_train, rr_train, y_train, X_test, rr_test, y_test,\n",
    "                             X_feat_train=None, X_feat_test=None, X_lt_feat_train=None, X_lt_feat_test=None,\n",
    "                             model_class=CNN2D, loss=torch.nn.CrossEntropyLoss,\n",
    "                             lr=1E-3, max_epochs=30, batch_size=1024,\n",
    "                             verbose=True, callbacks=None,\n",
    "                             class_weights=None, weight_decay=0., device=DEVICE, random_seed=0):\n",
    "    torch.manual_seed(random_seed)\n",
    "    \n",
    "    n_features = X_feat_train.shape[-1] if X_feat_train is not None else 0\n",
    "    n_lt_features = X_lt_feat_train.shape[-1] if X_lt_feat_train is not None else 0\n",
    "    \n",
    "    train_dict = {'X': X_train, 'X_rr': rr_train}\n",
    "    if X_feat_train is not None:\n",
    "        train_dict['X_feat'] = X_feat_train\n",
    "    if X_lt_feat_train is not None:\n",
    "        train_dict['X_lt_feat'] = X_lt_feat_train\n",
    "        \n",
    "    test_dict = {'X': X_test, 'X_rr': rr_test}\n",
    "    if X_feat_test is not None:\n",
    "        test_dict['X_feat'] = X_feat_test\n",
    "    if X_lt_feat_test is not None:\n",
    "        test_dict['X_lt_feat'] = X_lt_feat_test\n",
    "\n",
    "    kwargs = {}\n",
    "    if loss == FocalLoss:\n",
    "        kwargs[\"criterion__alpha\"] = class_weights\n",
    "    elif loss == MatrixWeightedCrossEntropy:\n",
    "        kwargs[\"criterion__weight_matrix\"] = class_weights\n",
    "    else:\n",
    "        kwargs[\"criterion__weight\"] = class_weights\n",
    "\n",
    "    model = skorch.NeuralNetClassifier(\n",
    "        model_class,\n",
    "        module__n_features=n_features,\n",
    "        module__n_lt_features=n_lt_features,\n",
    "        criterion=loss,\n",
    "        optimizer=torch.optim.Adam,\n",
    "        lr=lr,\n",
    "        max_epochs=max_epochs,\n",
    "        batch_size=batch_size,\n",
    "        train_split=predefined_split(Dataset(test_dict, y_test)),\n",
    "        verbose=1 if verbose else 0,\n",
    "        device=device,\n",
    "        callbacks=callbacks,\n",
    "        iterator_train__shuffle=True,\n",
    "        optimizer__weight_decay=weight_decay,\n",
    "        **kwargs\n",
    "    )\n",
    "\n",
    "    _ = model.fit(train_dict, y_train)\n",
    "    \n",
    "    y_true, y_pred = y_test, model.predict(test_dict)\n",
    "    bal_acc = balanced_accuracy_score(y_true, y_pred)\n",
    "\n",
    "    if verbose:\n",
    "        conf_mat = confusion_matrix(y_true, y_pred)\n",
    "        cm_train_plot = ConfusionMatrixDisplay(conf_mat / np.expand_dims(conf_mat.sum(axis=1), axis=1), display_labels=[num_to_label[i] for i in range(len(allowed_labels))])\n",
    "        cm_train_plot.plot(colorbar=False)\n",
    "        plt.show()\n",
    "    \n",
    "        print(classification_report(y_true, y_pred, digits=4))\n",
    "    return model, classification_report(y_true, y_pred, digits=4, output_dict=True), bal_acc, y_true, y_pred\n",
    "\n",
    "\n",
    "def balance_data(X, rr, X_feat, X_lt_feat, y, noise_std=None):\n",
    "    classes, counts = np.unique(y, return_counts=True)\n",
    "    max_count = np.max(counts)\n",
    "\n",
    "    X_oversampled = [X]\n",
    "    rr_oversampled = [rr]\n",
    "    X_feat_oversampled = [X_feat]\n",
    "    X_lt_feat_oversampled = [X_lt_feat]\n",
    "    y_oversampled = [y]\n",
    "\n",
    "    if noise_std is not None:\n",
    "        X_std = X.std() * noise_std\n",
    "        rr_std = rr.std() * noise_std\n",
    "        X_feat_std = X_feat.std() * noise_std\n",
    "        X_lt_feat_std = X_lt_feat.std() * noise_std\n",
    "\n",
    "    for class_id in classes:\n",
    "        class_indices = np.where(y == class_id)[0]\n",
    "        num_to_add = max_count - counts[class_id]\n",
    "        indices_to_add = np.random.choice(class_indices, num_to_add)\n",
    "            \n",
    "        X_selected = X[indices_to_add].copy()\n",
    "        rr_selected = rr[indices_to_add].copy()\n",
    "        X_feat_selected = X_feat[indices_to_add].copy()\n",
    "        X_lt_feat_selected = X_lt_feat[indices_to_add].copy()\n",
    "\n",
    "        if noise_std is not None:\n",
    "            X_selected += X_std * np.random.standard_normal(X_selected.shape)\n",
    "            rr_selected += rr_std * np.random.standard_normal(rr_selected.shape)\n",
    "            X_feat_selected += X_feat_std * np.random.standard_normal(X_feat_selected.shape)\n",
    "            X_lt_feat_selected += X_lt_feat_std * np.random.standard_normal(X_lt_feat_selected.shape)\n",
    "        \n",
    "        X_oversampled.append(X_selected)\n",
    "        rr_oversampled.append(rr_selected)\n",
    "        X_feat_oversampled.append(X_feat_selected)\n",
    "        X_lt_feat_oversampled.append(X_lt_feat_selected)\n",
    "        y_oversampled.append(np.ones(len(indices_to_add), dtype=np.int64) * class_id)\n",
    "\n",
    "    return (np.concatenate(X_oversampled),\n",
    "            np.concatenate(rr_oversampled),\n",
    "            np.concatenate(X_feat_oversampled),\n",
    "            np.concatenate(X_lt_feat_oversampled),\n",
    "            np.concatenate(y_oversampled))\n",
    "\n",
    "\n",
    "def print_results(reports, num_to_label=num_to_label, allowed_labels=allowed_labels):\n",
    "    precision_d = defaultdict(list)\n",
    "    recall_d = defaultdict(list)\n",
    "    f1_d = defaultdict(list)\n",
    "\n",
    "    for report in reports:\n",
    "        for i in allowed_labels:\n",
    "            precision_d[num_to_label[float(i)]].append(report[str(i)]['precision'])\n",
    "            recall_d[num_to_label[float(i)]].append(report[str(i)]['recall'])\n",
    "            f1_d[num_to_label[float(i)]].append(report[str(i)]['f1-score'])              \n",
    "    \n",
    "    for i in allowed_labels:\n",
    "        label = num_to_label[i]\n",
    "        print(f\"{label} -> Precision: {np.mean(precision_d[label]):.02%}±{100*np.std(precision_d[label]):.02f}; Recall: {np.mean(recall_d[label]):.02%}±{100*np.std(recall_d[label]):.02f}; F1-score: {np.mean(f1_d[label]):.02%}±{100*np.std(f1_d[label]):.02f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ecb321cb-b1b4-4b84-b701-321d2eed153e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 0\n",
      "  epoch    train_loss    valid_acc    valid_acc_bal    valid_f1    valid_loss    cp     dur\n",
      "-------  ------------  -----------  ---------------  ----------  ------------  ----  ------\n",
      "      1        \u001b[36m1.2428\u001b[0m       \u001b[32m0.3700\u001b[0m           \u001b[35m0.3608\u001b[0m      \u001b[31m0.2402\u001b[0m        \u001b[94m2.9264\u001b[0m     +  4.4277\n",
      "      2        \u001b[36m1.0000\u001b[0m       \u001b[32m0.5690\u001b[0m           \u001b[35m0.3984\u001b[0m      \u001b[31m0.3200\u001b[0m        \u001b[94m2.1464\u001b[0m     +  4.0982\n",
      "      3        \u001b[36m0.8957\u001b[0m       0.5159           \u001b[35m0.4738\u001b[0m      \u001b[31m0.3279\u001b[0m        \u001b[94m1.2319\u001b[0m     +  3.9634\n",
      "      4        \u001b[36m0.8365\u001b[0m       0.5651           \u001b[35m0.4942\u001b[0m      \u001b[31m0.3530\u001b[0m        \u001b[94m1.0954\u001b[0m     +  4.1235\n",
      "      5        \u001b[36m0.7925\u001b[0m       0.5118           0.4892      0.3307        1.1023        3.9755\n",
      "      6        \u001b[36m0.7608\u001b[0m       0.5511           0.4644      0.3404        1.2940        4.1174\n",
      "      7        \u001b[36m0.7372\u001b[0m       0.5205           0.4903      0.3346        1.1572        3.9694\n",
      "      8        \u001b[36m0.7178\u001b[0m       0.5179           \u001b[35m0.5029\u001b[0m      0.3374        1.1182     +  4.1232\n",
      "      9        \u001b[36m0.7002\u001b[0m       \u001b[32m0.5751\u001b[0m           0.4901      \u001b[31m0.3591\u001b[0m        1.1319        3.9700\n",
      "     10        \u001b[36m0.6874\u001b[0m       \u001b[32m0.5854\u001b[0m           0.4703      0.3562        1.2353        4.1078\n",
      "     11        \u001b[36m0.6766\u001b[0m       0.5847           0.4777      0.3591        1.1735        3.9649\n",
      "     12        \u001b[36m0.6673\u001b[0m       0.5441           \u001b[35m0.5172\u001b[0m      0.3524        1.1193     +  4.1033\n",
      "     13        \u001b[36m0.6542\u001b[0m       \u001b[32m0.6011\u001b[0m           0.4767      \u001b[31m0.3643\u001b[0m        1.1964        3.9739\n",
      "     14        \u001b[36m0.6481\u001b[0m       0.5793           0.4769      0.3554        1.2059        4.1047\n",
      "     15        \u001b[36m0.6406\u001b[0m       0.5940           0.4810      0.3615        1.2621        3.9658\n",
      "     16        \u001b[36m0.6322\u001b[0m       \u001b[32m0.6169\u001b[0m           0.4610      \u001b[31m0.3680\u001b[0m        1.2539        4.1086\n",
      "     17        \u001b[36m0.6257\u001b[0m       0.5744           0.4978      0.3578        1.1676        3.9706\n",
      "     18        \u001b[36m0.6214\u001b[0m       0.5733           0.4987      0.3653        1.1341        4.1115\n",
      "     19        \u001b[36m0.6147\u001b[0m       0.5668           0.4987      0.3580        1.1607        3.9731\n",
      "     20        \u001b[36m0.6090\u001b[0m       0.5988           0.4660      0.3641        1.2252        4.1082\n",
      "     21        \u001b[36m0.6046\u001b[0m       0.5893           0.5016      \u001b[31m0.3681\u001b[0m        1.1414        3.9721\n",
      "     22        \u001b[36m0.5992\u001b[0m       0.5841           0.4854      0.3618        1.2012        4.1123\n",
      "     23        \u001b[36m0.5957\u001b[0m       \u001b[32m0.6209\u001b[0m           0.4734      \u001b[31m0.3727\u001b[0m        1.2233        3.9729\n",
      "     24        \u001b[36m0.5938\u001b[0m       0.5992           0.4900      \u001b[31m0.3741\u001b[0m        1.1646        4.1120\n",
      "     25        0.5942       0.5593           0.5075      0.3572        1.1340        3.9726\n",
      "     26        \u001b[36m0.5852\u001b[0m       0.5758           0.4782      0.3613        1.2373        4.1081\n",
      "     27        \u001b[36m0.5788\u001b[0m       0.6175           0.4803      \u001b[31m0.3756\u001b[0m        1.2078        3.9695\n",
      "     28        \u001b[36m0.5753\u001b[0m       0.5862           0.4760      0.3663        1.2320        4.1099\n",
      "     29        \u001b[36m0.5741\u001b[0m       0.5970           0.4888      0.3713        1.1696        4.1152\n",
      "     30        \u001b[36m0.5710\u001b[0m       0.5835           0.5030      0.3666        1.1389        3.9687\n",
      "     31        \u001b[36m0.5674\u001b[0m       0.6056           0.4665      0.3725        1.3216        4.1133\n",
      "     32        \u001b[36m0.5641\u001b[0m       0.5801           0.4908      0.3681        1.1867        3.9700\n",
      "     33        \u001b[36m0.5609\u001b[0m       \u001b[32m0.6222\u001b[0m           0.4955      \u001b[31m0.3816\u001b[0m        1.1762        4.1135\n",
      "     34        \u001b[36m0.5592\u001b[0m       0.6063           0.4877      0.3741        1.2043        3.9714\n",
      "     35        \u001b[36m0.5566\u001b[0m       0.5955           0.4828      0.3705        1.3319        4.1105\n",
      "     36        \u001b[36m0.5534\u001b[0m       0.5965           0.4802      0.3708        1.2427        3.9735\n",
      "     37        \u001b[36m0.5494\u001b[0m       0.5824           0.4999      0.3723        1.1945        4.1128\n",
      "     38        \u001b[36m0.5470\u001b[0m       0.5956           0.4948      0.3776        1.1982        3.9704\n",
      "     39        \u001b[36m0.5455\u001b[0m       0.5778           0.5023      0.3712        1.2003        4.1145\n",
      "     40        \u001b[36m0.5434\u001b[0m       0.5808           \u001b[35m0.5198\u001b[0m      0.3720        1.1677     +  3.9739\n",
      "     41        0.5437       0.5796           0.4960      0.3715        1.2227        4.1150\n",
      "     42        \u001b[36m0.5383\u001b[0m       0.5791           0.4976      0.3771        1.2313        3.9724\n",
      "     43        \u001b[36m0.5356\u001b[0m       0.5938           0.4859      0.3693        1.2947        4.1190\n",
      "     44        \u001b[36m0.5329\u001b[0m       \u001b[32m0.6302\u001b[0m           0.4891      \u001b[31m0.3894\u001b[0m        1.2420        3.9729\n",
      "     45        \u001b[36m0.5290\u001b[0m       0.5881           0.5062      0.3746        1.2094        4.1142\n",
      "     46        0.5293       0.6143           0.4841      0.3857        1.2926        3.9782\n",
      "     47        \u001b[36m0.5259\u001b[0m       0.5868           0.5060      0.3767        1.2193        4.1172\n",
      "     48        \u001b[36m0.5245\u001b[0m       0.6005           0.4920      0.3750        1.3198        3.9729\n",
      "     49        \u001b[36m0.5229\u001b[0m       \u001b[32m0.6374\u001b[0m           0.4716      0.3874        1.3583        4.1140\n",
      "     50        \u001b[36m0.5209\u001b[0m       0.6068           0.4899      0.3807        1.3150        4.1149\n",
      "     51        \u001b[36m0.5184\u001b[0m       0.6056           0.4846      0.3791        1.3435        3.9698\n",
      "     52        \u001b[36m0.5170\u001b[0m       0.6065           0.5022      0.3864        1.2536        4.1124\n",
      "     53        \u001b[36m0.5162\u001b[0m       0.6016           0.5011      0.3827        1.2651        3.9754\n",
      "     54        \u001b[36m0.5131\u001b[0m       0.6262           0.4897      0.3865        1.3305        4.1173\n",
      "     55        \u001b[36m0.5107\u001b[0m       0.5737           0.5143      0.3727        1.2595        3.9737\n",
      "     56        \u001b[36m0.5085\u001b[0m       0.5925           0.5076      0.3791        1.2757        4.1126\n",
      "     57        \u001b[36m0.5077\u001b[0m       0.5814           0.4926      0.3753        1.3036        3.9744\n",
      "     58        \u001b[36m0.5054\u001b[0m       0.6038           0.4952      0.3838        1.3125        4.1168\n",
      "     59        \u001b[36m0.5033\u001b[0m       0.6219           0.4985      0.3876        1.2863        3.9719\n",
      "     60        \u001b[36m0.5015\u001b[0m       0.6058           0.5101      0.3832        1.2740        4.1137\n",
      "     61        \u001b[36m0.4990\u001b[0m       0.6226           0.4988      0.3884        1.2663        3.9750\n",
      "     62        \u001b[36m0.4979\u001b[0m       0.6046           0.4903      0.3849        1.3220        4.1132\n",
      "     63        0.4990       0.6265           0.4851      0.3875        1.3479        3.9745\n",
      "     64        \u001b[36m0.4952\u001b[0m       0.6221           0.4938      0.3893        1.3176        4.1143\n",
      "     65        \u001b[36m0.4950\u001b[0m       0.6116           0.5056      0.3853        1.3071        3.9777\n",
      "     66        \u001b[36m0.4918\u001b[0m       0.6059           0.5054      0.3869        1.2841        4.1210\n",
      "     67        0.4920       0.6129           0.4912      0.3887        1.3488        3.9773\n",
      "     68        \u001b[36m0.4895\u001b[0m       0.6112           0.4961      0.3843        1.3403        4.1134\n",
      "     69        \u001b[36m0.4864\u001b[0m       0.6284           0.4931      \u001b[31m0.3926\u001b[0m        1.3093        3.9724\n",
      "     70        \u001b[36m0.4861\u001b[0m       0.6105           0.4967      0.3841        1.3408        4.1148\n",
      "     71        \u001b[36m0.4835\u001b[0m       0.6289           0.4763      0.3901        1.3846        3.9734\n",
      "     72        \u001b[36m0.4824\u001b[0m       0.6113           0.4964      0.3874        1.3204        4.1160\n",
      "     73        \u001b[36m0.4812\u001b[0m       0.6116           0.5142      0.3893        1.3135        3.9672\n",
      "     74        \u001b[36m0.4789\u001b[0m       0.6251           0.5009      0.3921        1.3207        4.1156\n",
      "     75        0.4800       0.6059           0.4956      0.3889        1.3646        3.9766\n",
      "     76        \u001b[36m0.4772\u001b[0m       \u001b[32m0.6382\u001b[0m           0.4879      0.3896        1.3835        4.1199\n",
      "     77        \u001b[36m0.4771\u001b[0m       0.5856           \u001b[35m0.5203\u001b[0m      0.3759        1.3188     +  3.9754\n",
      "     78        \u001b[36m0.4752\u001b[0m       0.6234           0.4868      0.3856        1.3863        4.1274\n",
      "     79        \u001b[36m0.4719\u001b[0m       0.5971           0.4931      0.3795        1.4311        3.9731\n",
      "     80        0.4737       \u001b[32m0.6464\u001b[0m           0.4762      \u001b[31m0.3973\u001b[0m        1.4295        4.1154\n",
      "     81        \u001b[36m0.4690\u001b[0m       0.6262           0.4840      0.3891        1.4239        3.9780\n",
      "     82        \u001b[36m0.4683\u001b[0m       0.6377           0.4684      0.3938        1.4822        4.1149\n",
      "     83        0.4689       0.6327           0.4975      0.3957        1.3791        3.9739\n",
      "     84        \u001b[36m0.4677\u001b[0m       0.6347           0.4803      0.3927        1.4362        4.1332\n",
      "     85        \u001b[36m0.4658\u001b[0m       0.6257           0.5100      0.3907        1.3579        3.9755\n",
      "     86        0.4676       0.6303           0.4835      0.3965        1.4242        4.1136\n",
      "     87        \u001b[36m0.4652\u001b[0m       0.5918           0.5072      0.3790        1.3632        3.9735\n",
      "     88        0.4677       0.6289           0.5006      0.3950        1.4147        4.1173\n",
      "     89        \u001b[36m0.4634\u001b[0m       0.6424           0.4821      0.3915        1.5167        3.9776\n",
      "     90        \u001b[36m0.4620\u001b[0m       \u001b[32m0.6527\u001b[0m           0.4902      \u001b[31m0.3998\u001b[0m        1.3977        4.1211\n",
      "     91        \u001b[36m0.4591\u001b[0m       0.6349           0.5037      0.3893        1.4283        3.9757\n",
      "     92        \u001b[36m0.4577\u001b[0m       0.6030           0.4858      0.3870        1.4327        4.1169\n",
      "     93        \u001b[36m0.4561\u001b[0m       0.6246           0.4934      0.3932        1.4480        3.9845\n",
      "     94        0.4561       \u001b[32m0.6590\u001b[0m           0.4827      0.3987        1.4984        4.1104\n",
      "     95        \u001b[36m0.4524\u001b[0m       0.6036           0.5000      0.3869        1.4379        3.9714\n",
      "     96        \u001b[36m0.4506\u001b[0m       0.6112           0.4917      0.3857        1.4877        4.1112\n",
      "     97        \u001b[36m0.4495\u001b[0m       0.6095           0.5148      0.3898        1.4127        3.9703\n",
      "     98        \u001b[36m0.4494\u001b[0m       0.6027           0.5065      0.3871        1.4381        4.1102\n",
      "     99        \u001b[36m0.4473\u001b[0m       0.6267           0.5020      0.3945        1.4353        3.9737\n",
      "    100        \u001b[36m0.4467\u001b[0m       0.6014           0.5082      0.3823        1.4446        4.1029\n",
      "    101        0.4478       0.6332           0.5076      0.3907        1.4294        3.9700\n",
      "    102        \u001b[36m0.4465\u001b[0m       0.6544           0.5054      0.3968        1.4753        4.1150\n",
      "    103        \u001b[36m0.4435\u001b[0m       0.6106           0.5040      0.3814        1.4698        4.1099\n",
      "    104        \u001b[36m0.4419\u001b[0m       0.6489           0.4842      0.3947        1.5488        3.9642\n",
      "    105        0.4428       0.6191           0.4904      0.3907        1.4712        4.1082\n",
      "    106        \u001b[36m0.4396\u001b[0m       0.5893           0.5005      0.3849        1.4414        3.9693\n",
      "    107        \u001b[36m0.4392\u001b[0m       0.6346           0.4844      0.3977        1.4710        4.1167\n",
      "    108        \u001b[36m0.4379\u001b[0m       0.6556           0.4736      0.3993        1.5864        3.9698\n",
      "    109        \u001b[36m0.4371\u001b[0m       0.6369           0.4974      0.3948        1.4420        4.1135\n",
      "    110        \u001b[36m0.4364\u001b[0m       0.6193           0.5014      0.3877        1.4831        3.9712\n",
      "    111        \u001b[36m0.4323\u001b[0m       0.6370           0.4905      0.3961        1.4802        4.1139\n",
      "    112        \u001b[36m0.4323\u001b[0m       0.6452           0.4723      0.3993        1.5565        3.9733\n",
      "    113        0.4331       0.6389           0.4874      \u001b[31m0.4004\u001b[0m        1.5055        4.1130\n",
      "    114        \u001b[36m0.4300\u001b[0m       0.6235           0.5098      0.3967        1.4548        3.9740\n",
      "    115        \u001b[36m0.4272\u001b[0m       0.6074           0.5101      0.3903        1.4617        4.1119\n",
      "    116        \u001b[36m0.4267\u001b[0m       0.6000           0.5125      0.3808        1.4767        3.9698\n",
      "    117        0.4293       0.6329           0.4912      0.3950        1.5384        4.1106\n",
      "    118        0.4268       0.6182           0.5191      0.3897        1.4406        3.9677\n",
      "    119        \u001b[36m0.4252\u001b[0m       0.6196           \u001b[35m0.5256\u001b[0m      0.3914        1.4383     +  4.1152\n",
      "    120        0.4308       0.5638           \u001b[35m0.5327\u001b[0m      0.3740        1.4296     +  3.9790\n",
      "    121        0.4311       0.5798           0.5153      0.3778        1.4608        4.1212\n",
      "    122        \u001b[36m0.4250\u001b[0m       0.6317           0.4836      0.3949        1.5667        3.9670\n",
      "    123        \u001b[36m0.4221\u001b[0m       0.6295           0.4897      0.3916        1.5528        4.1089\n",
      "    124        \u001b[36m0.4210\u001b[0m       \u001b[32m0.6693\u001b[0m           0.4976      \u001b[31m0.4083\u001b[0m        1.5447        3.9713\n",
      "    125        \u001b[36m0.4186\u001b[0m       0.6423           0.4944      0.4000        1.5577        4.1268\n",
      "    126        \u001b[36m0.4158\u001b[0m       0.6433           0.4727      0.3951        1.6251        3.9769\n",
      "    127        0.4183       0.6231           0.5032      0.3892        1.5788        4.1241\n",
      "    128        0.4161       0.6416           0.4996      0.3993        1.5681        3.9795\n",
      "    129        0.4161       0.6151           0.5115      0.3856        1.5375        4.1288\n",
      "    130        \u001b[36m0.4152\u001b[0m       0.6498           0.4933      0.4058        1.5897        3.9709\n",
      "    131        \u001b[36m0.4132\u001b[0m       0.6006           0.5054      0.3871        1.5752        4.1119\n",
      "    132        \u001b[36m0.4126\u001b[0m       0.6399           0.4686      0.3985        1.6154        3.9670\n",
      "    133        0.4161       0.6411           0.4836      0.4029        1.6477        4.1136\n",
      "    134        \u001b[36m0.4114\u001b[0m       0.5938           0.4974      0.3871        1.5621        3.9682\n",
      "    135        \u001b[36m0.4114\u001b[0m       0.6273           0.5032      0.3972        1.5460        4.1108\n",
      "    136        \u001b[36m0.4108\u001b[0m       0.6224           0.5002      0.3893        1.6264        3.9664\n",
      "    137        \u001b[36m0.4088\u001b[0m       0.6565           0.4971      0.3987        1.5917        4.1091\n",
      "    138        \u001b[36m0.4064\u001b[0m       0.6294           0.5158      0.3977        1.5274        3.9685\n",
      "    139        0.4103       0.5894           0.4814      0.3794        1.5878        4.1123\n",
      "    140        \u001b[36m0.4053\u001b[0m       0.6287           0.5164      0.3954        1.5253        3.9655\n",
      "    141        \u001b[36m0.4047\u001b[0m       0.6525           0.4934      0.4015        1.6144        4.1121\n",
      "    142        0.4060       0.6434           0.4579      0.3949        1.7595        3.9718\n",
      "    143        \u001b[36m0.4044\u001b[0m       0.6170           0.4921      0.3940        1.5977        4.1093\n",
      "    144        \u001b[36m0.3999\u001b[0m       0.6194           0.4842      0.3926        1.6226        3.9734\n",
      "    145        0.4006       0.6282           0.4806      0.3935        1.6888        4.1046\n",
      "    146        \u001b[36m0.3984\u001b[0m       0.6122           0.4919      0.3860        1.6699        3.9719\n",
      "    147        \u001b[36m0.3966\u001b[0m       0.6237           0.4916      0.3959        1.6504        4.1092\n",
      "    148        0.3980       0.6479           0.4765      0.3970        1.7113        3.9703\n",
      "    149        \u001b[36m0.3958\u001b[0m       0.6629           0.4769      0.4002        1.7352        4.1133\n",
      "    150        \u001b[36m0.3953\u001b[0m       \u001b[32m0.6791\u001b[0m           0.4912      \u001b[31m0.4094\u001b[0m        1.6299        3.9678\n",
      "    151        0.3960       0.6618           0.4820      0.4036        1.6946        4.1119\n",
      "    152        \u001b[36m0.3928\u001b[0m       0.6547           0.4744      0.3991        1.7134        3.9695\n",
      "    153        \u001b[36m0.3912\u001b[0m       0.6460           0.4874      0.3953        1.6674        4.1123\n",
      "    154        0.3928       0.6425           0.4962      0.3975        1.6591        3.9663\n",
      "    155        0.3933       0.6755           0.4796      0.4052        1.6739        4.1150\n",
      "    156        0.3915       0.6168           0.4786      0.3916        1.6878        3.9703\n",
      "    157        \u001b[36m0.3902\u001b[0m       0.6287           0.4726      0.3949        1.7526        4.1109\n",
      "    158        \u001b[36m0.3869\u001b[0m       0.6619           0.4593      0.3999        1.7776        3.9697\n",
      "    159        0.3914       0.6628           0.4646      0.4008        1.7709        4.1124\n",
      "    160        0.3898       0.6176           0.4772      0.3903        1.6995        3.9725\n",
      "    161        \u001b[36m0.3868\u001b[0m       0.6542           0.4722      0.4035        1.7706        4.1139\n",
      "    162        0.3876       0.6468           0.4728      0.3967        1.7226        3.9728\n",
      "    163        \u001b[36m0.3849\u001b[0m       0.6291           0.4821      0.3938        1.7809        4.1114\n",
      "    164        \u001b[36m0.3830\u001b[0m       0.6481           0.4582      0.3920        1.8122        3.9689\n",
      "    165        \u001b[36m0.3823\u001b[0m       0.6148           0.4666      0.3870        1.8271        4.1117\n",
      "    166        0.3826       0.6477           0.4751      0.3951        1.7932        3.9725\n",
      "    167        0.3846       0.6263           0.4715      0.3915        1.7570        4.1096\n",
      "    168        \u001b[36m0.3806\u001b[0m       0.6545           0.4638      0.3971        1.8259        3.9689\n",
      "    169        \u001b[36m0.3793\u001b[0m       0.6458           0.4597      0.3915        1.8200        4.1131\n",
      "    170        \u001b[36m0.3781\u001b[0m       0.6220           0.4970      0.3932        1.7452        3.9682\n",
      "    171        \u001b[36m0.3772\u001b[0m       0.6444           0.4888      0.3927        1.7355        4.1126\n",
      "    172        0.3781       0.6355           0.4770      0.3948        1.7514        3.9696\n",
      "    173        \u001b[36m0.3755\u001b[0m       0.6457           0.4797      0.3943        1.7536        4.1073\n",
      "    174        \u001b[36m0.3749\u001b[0m       0.6697           0.4547      0.3950        1.8983        3.9683\n",
      "    175        0.3760       0.6121           0.4940      0.3840        1.7378        4.1140\n",
      "    176        0.3760       0.6567           0.4646      0.3943        1.8132        3.9712\n",
      "    177        \u001b[36m0.3741\u001b[0m       0.6507           0.4595      0.3943        1.8864        4.1094\n",
      "    178        \u001b[36m0.3700\u001b[0m       0.6443           0.4761      0.3946        1.7850        3.9702\n",
      "    179        0.3705       0.6537           0.4865      0.3963        1.8159        4.1077\n",
      "    180        0.3786       0.6537           0.4742      0.3914        1.7773        3.9717\n",
      "    181        0.3703       0.6643           0.4800      0.4003        1.8114        4.1066\n",
      "    182        0.3704       \u001b[32m0.6792\u001b[0m           0.4896      0.4043        1.7993        3.9665\n",
      "    183        0.3703       0.6696           0.4596      0.3994        1.9074        4.1213\n",
      "    184        \u001b[36m0.3697\u001b[0m       0.6385           0.4651      0.3879        1.8710        3.9797\n",
      "    185        \u001b[36m0.3672\u001b[0m       0.6782           0.4703      0.4003        1.9155        4.1219\n",
      "    186        \u001b[36m0.3671\u001b[0m       0.6597           0.4928      0.3995        1.7527        3.9788\n",
      "    187        \u001b[36m0.3649\u001b[0m       0.6718           0.4581      0.3961        1.9136        4.1163\n",
      "    188        \u001b[36m0.3629\u001b[0m       0.6483           0.4666      0.3903        1.8856        3.9668\n",
      "    189        0.3648       0.6769           0.4484      0.3937        1.9494        4.1104\n",
      "    190        0.3667       0.6453           0.4653      0.3934        1.8994        3.9761\n",
      "    191        0.3648       0.5970           0.4586      0.3718        1.9021        4.1090\n",
      "    192        0.3758       0.6561           0.4459      0.3875        1.9646        3.9702\n",
      "    193        0.3666       0.6327           0.4192      0.3722        2.2194        4.1150\n",
      "    194        0.3697       0.6391           0.4245      0.3813        2.2223        3.9716\n",
      "    195        0.3673       0.6592           0.4779      0.4028        1.8467        4.1186\n",
      "    196        0.3679       \u001b[32m0.6831\u001b[0m           0.4715      0.4072        1.8813        3.9711\n",
      "    197        0.3646       \u001b[32m0.6929\u001b[0m           0.4544      0.4018        1.9559        4.1101\n",
      "    198        \u001b[36m0.3588\u001b[0m       0.6725           0.4527      0.3954        1.9801        3.9704\n",
      "    199        \u001b[36m0.3570\u001b[0m       0.6477           0.4765      0.3932        1.8815        4.1104\n",
      "    200        0.3571       0.6912           0.4521      0.4016        2.0071        3.9710\n"
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    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.9318    0.5699    0.7072     43968\n",
      "           1     0.1634    0.5261    0.2494      1817\n",
      "           2     0.0990    0.5022    0.1654      3204\n",
      "\n",
      "    accuracy                         0.5638     48989\n",
      "   macro avg     0.3981    0.5327    0.3740     48989\n",
      "weighted avg     0.8488    0.5638    0.6548     48989\n",
      "\n",
      "Time: 856.45s; Balanced accuracy: 53.27%\n",
      "N -> Precision: 93.18%±0.00; Recall: 56.99%±0.00; F1-score: 70.72%±0.00\n",
      "V -> Precision: 9.90%±0.00; Recall: 50.22%±0.00; F1-score: 16.54%±0.00\n",
      "S -> Precision: 16.34%±0.00; Recall: 52.61%±0.00; F1-score: 24.94%±0.00\n",
      "Balanced accuracy: 53.27% ± 0.000000\n"
     ]
    }
   ],
   "source": [
    "callbacks = [\n",
    "    #LRScheduler(policy=StepLR, step_size=20, gamma=.5),\n",
    "    EpochScoring(scoring=make_scorer(f1_score, average=\"macro\"), lower_is_better=False, name=\"valid_f1\"),\n",
    "    EpochScoring(scoring=make_scorer(balanced_accuracy_score), lower_is_better=False, name=\"valid_acc_bal\"),\n",
    "    Checkpoint(f_pickle='models/checkpoint.pkl', load_best=True, monitor='valid_acc_bal_best')\n",
    "]\n",
    "\n",
    "n_iterations = 1\n",
    "\n",
    "n_features = 5\n",
    "\n",
    "class_weights = torch.from_numpy(compute_class_weight('balanced', classes=np.unique(y_train), y=y_train)).to(torch.float32).to(DEVICE)\n",
    "#class_weights[2] = 2 * class_weights[0]\n",
    "#class_weights[1] /= 6\n",
    "#class_weights = torch.Tensor([1., 10., 5.]).to(torch.float32).to(DEVICE)\n",
    "#class_weights = torch.Tensor([[1, 50, 10], [50, 50, 50], [10, 10, 10]]).to(torch.float32).to(DEVICE)\n",
    "#class_weights = None\n",
    "\n",
    "reports = []\n",
    "balanced_accuracies = []\n",
    "predictions = []\n",
    "for i in range(n_iterations):\n",
    "    print(f\"Iteration {i}\")\n",
    "    ts = time()\n",
    "    model, report, bal_acc, y_true, y_pred = train_and_evaluate_model(\n",
    "        X_cwt_train, rr_train, y_train, X_cwt_test, rr_test, y_test,\n",
    "        X_feat_train=X_feat_train[:, :n_features], X_feat_test=X_feat_test[:, :n_features],\n",
    "        # X_oversampled, rr_oversampled, y_oversampled, X_cwt_test, rr_test, y_test,\n",
    "        # X_feat_train=X_feat_oversampled[:, :n_features], X_feat_test=X_feat_test[:, :n_features],\n",
    "        max_epochs=200, class_weights=class_weights, loss=torch.nn.CrossEntropyLoss, weight_decay=0, lr=1E-4, batch_size=4096,\n",
    "        model_class=CNN2D, callbacks=callbacks, verbose=True, random_seed=i\n",
    "    )\n",
    "    balanced_accuracies.append(bal_acc)\n",
    "    reports.append(report)\n",
    "    predictions.append((y_true, y_pred))\n",
    "    print(f\"Time: {time() - ts:.02f}s; Balanced accuracy: {bal_acc * 100:.02f}%\")\n",
    "\n",
    "print_results(reports)\n",
    "print(f\"Balanced accuracy: {np.mean(balanced_accuracies) * 100:.02f}% ± {np.std(balanced_accuracies) * 100:02f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bfdaaf6d-08a7-4bfe-8670-e4e683299033",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f467be26d50>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_cwt_train[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "793b355e-9bec-4a24-bace-4966459ac15a",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## Balanced batching"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "af434ae3-3cd3-42cd-98fe-a591de362c8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "class BalancedBatchSampler(Sampler):\n",
    "    def __init__(self, labels, n_classes, n_samples):\n",
    "        self.labels = labels\n",
    "        self.n_classes = n_classes\n",
    "        self.n_samples = n_samples\n",
    "        self.batch_size = n_samples * n_classes\n",
    "\n",
    "        self.class_indices = [np.where(self.labels == i)[0] for i in range(n_classes)]\n",
    "        for i in self.class_indices:\n",
    "            assert len(i) > n_samples, \"Not enough samples of class to sample without replacement\"\n",
    "\n",
    "    def __iter__(self):\n",
    "        while True:\n",
    "            indices = []\n",
    "            for i in range(self.n_classes):\n",
    "                indices += list(np.random.choice(self.class_indices[i], self.n_samples, replace=False))\n",
    "            shuffle(indices)\n",
    "            yield indices\n",
    "\n",
    "    def __len__(self):\n",
    "        return 2**64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "38c0c80a-ba4f-47c3-a04d-2a9561a45fd5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [1/200], Train Loss: 1.2943, Val Loss: 0.1491, Val Balanced Acc: 0.5318, Val F1 Score: 0.9125, Val Acc: 0.9219, Time: 6.44 sec\n",
      "Epoch [2/200], Train Loss: 0.1700, Val Loss: 0.0890, Val Balanced Acc: 0.6020, Val F1 Score: 0.9278, Val Acc: 0.9354, Time: 6.07 sec\n",
      "Epoch [3/200], Train Loss: 0.0609, Val Loss: 0.0675, Val Balanced Acc: 0.6715, Val F1 Score: 0.9412, Val Acc: 0.9487, Time: 6.21 sec\n",
      "Epoch [4/200], Train Loss: 0.0610, Val Loss: 0.0591, Val Balanced Acc: 0.6992, Val F1 Score: 0.9468, Val Acc: 0.9529, Time: 6.11 sec\n",
      "Epoch [5/200], Train Loss: 0.0660, Val Loss: 0.0556, Val Balanced Acc: 0.7265, Val F1 Score: 0.9503, Val Acc: 0.9534, Time: 6.09 sec\n",
      "Epoch [6/200], Train Loss: 0.0694, Val Loss: 0.0524, Val Balanced Acc: 0.7556, Val F1 Score: 0.9550, Val Acc: 0.9587, Time: 6.23 sec\n",
      "Epoch [7/200], Train Loss: 0.0922, Val Loss: 0.0486, Val Balanced Acc: 0.7575, Val F1 Score: 0.9579, Val Acc: 0.9615, Time: 6.10 sec\n",
      "Epoch [8/200], Train Loss: 0.0453, Val Loss: 0.0477, Val Balanced Acc: 0.7933, Val F1 Score: 0.9630, Val Acc: 0.9654, Time: 6.21 sec\n",
      "Epoch [9/200], Train Loss: 0.0300, Val Loss: 0.0458, Val Balanced Acc: 0.8081, Val F1 Score: 0.9651, Val Acc: 0.9669, Time: 6.26 sec\n",
      "Epoch [10/200], Train Loss: 0.0278, Val Loss: 0.0446, Val Balanced Acc: 0.8097, Val F1 Score: 0.9656, Val Acc: 0.9674, Time: 6.20 sec\n",
      "Epoch [11/200], Train Loss: 0.0177, Val Loss: 0.0419, Val Balanced Acc: 0.8436, Val F1 Score: 0.9692, Val Acc: 0.9700, Time: 6.02 sec\n",
      "Epoch [12/200], Train Loss: 0.0160, Val Loss: 0.0413, Val Balanced Acc: 0.8639, Val F1 Score: 0.9721, Val Acc: 0.9726, Time: 6.20 sec\n",
      "Epoch [13/200], Train Loss: 0.0355, Val Loss: 0.0422, Val Balanced Acc: 0.8777, Val F1 Score: 0.9724, Val Acc: 0.9722, Time: 6.10 sec\n",
      "Epoch [14/200], Train Loss: 0.0142, Val Loss: 0.0412, Val Balanced Acc: 0.8656, Val F1 Score: 0.9738, Val Acc: 0.9744, Time: 6.39 sec\n",
      "Epoch [15/200], Train Loss: 0.0132, Val Loss: 0.0418, Val Balanced Acc: 0.8455, Val F1 Score: 0.9722, Val Acc: 0.9733, Time: 6.30 sec\n",
      "Epoch [16/200], Train Loss: 0.0220, Val Loss: 0.0416, Val Balanced Acc: 0.8645, Val F1 Score: 0.9739, Val Acc: 0.9747, Time: 6.00 sec\n",
      "Epoch [17/200], Train Loss: 0.0163, Val Loss: 0.0419, Val Balanced Acc: 0.8749, Val F1 Score: 0.9730, Val Acc: 0.9733, Time: 6.39 sec\n",
      "Epoch [18/200], Train Loss: 0.0168, Val Loss: 0.0397, Val Balanced Acc: 0.8694, Val F1 Score: 0.9735, Val Acc: 0.9739, Time: 6.09 sec\n",
      "Epoch [19/200], Train Loss: 0.0141, Val Loss: 0.0401, Val Balanced Acc: 0.8697, Val F1 Score: 0.9752, Val Acc: 0.9759, Time: 6.22 sec\n",
      "Epoch [20/200], Train Loss: 0.0363, Val Loss: 0.0423, Val Balanced Acc: 0.8404, Val F1 Score: 0.9728, Val Acc: 0.9743, Time: 6.16 sec\n",
      "Epoch [21/200], Train Loss: 0.0656, Val Loss: 0.0439, Val Balanced Acc: 0.8229, Val F1 Score: 0.9699, Val Acc: 0.9716, Time: 6.24 sec\n",
      "Epoch [22/200], Train Loss: 0.0243, Val Loss: 0.0410, Val Balanced Acc: 0.8784, Val F1 Score: 0.9746, Val Acc: 0.9748, Time: 6.19 sec\n",
      "Epoch [23/200], Train Loss: 0.0136, Val Loss: 0.0403, Val Balanced Acc: 0.8896, Val F1 Score: 0.9756, Val Acc: 0.9756, Time: 6.39 sec\n",
      "Epoch [24/200], Train Loss: 0.0076, Val Loss: 0.0396, Val Balanced Acc: 0.8804, Val F1 Score: 0.9755, Val Acc: 0.9757, Time: 6.27 sec\n",
      "Epoch [25/200], Train Loss: 0.0315, Val Loss: 0.0421, Val Balanced Acc: 0.8538, Val F1 Score: 0.9741, Val Acc: 0.9752, Time: 6.06 sec\n",
      "Epoch [26/200], Train Loss: 0.0276, Val Loss: 0.0414, Val Balanced Acc: 0.8936, Val F1 Score: 0.9763, Val Acc: 0.9764, Time: 6.23 sec\n",
      "Epoch [27/200], Train Loss: 0.0572, Val Loss: 0.0420, Val Balanced Acc: 0.8703, Val F1 Score: 0.9743, Val Acc: 0.9748, Time: 6.20 sec\n",
      "Epoch [28/200], Train Loss: 0.0147, Val Loss: 0.0391, Val Balanced Acc: 0.8769, Val F1 Score: 0.9760, Val Acc: 0.9764, Time: 6.21 sec\n",
      "Epoch [29/200], Train Loss: 0.0091, Val Loss: 0.0388, Val Balanced Acc: 0.8949, Val F1 Score: 0.9759, Val Acc: 0.9756, Time: 6.34 sec\n",
      "Epoch [30/200], Train Loss: 0.0161, Val Loss: 0.0422, Val Balanced Acc: 0.8337, Val F1 Score: 0.9717, Val Acc: 0.9730, Time: 6.01 sec\n",
      "Epoch [31/200], Train Loss: 0.0065, Val Loss: 0.0397, Val Balanced Acc: 0.8814, Val F1 Score: 0.9758, Val Acc: 0.9761, Time: 6.27 sec\n",
      "Epoch [32/200], Train Loss: 0.0082, Val Loss: 0.0394, Val Balanced Acc: 0.8803, Val F1 Score: 0.9764, Val Acc: 0.9768, Time: 6.36 sec\n",
      "Epoch [33/200], Train Loss: 0.0185, Val Loss: 0.0407, Val Balanced Acc: 0.8912, Val F1 Score: 0.9770, Val Acc: 0.9772, Time: 6.33 sec\n",
      "Epoch [34/200], Train Loss: 0.0421, Val Loss: 0.0393, Val Balanced Acc: 0.8898, Val F1 Score: 0.9754, Val Acc: 0.9754, Time: 6.29 sec\n",
      "Epoch [35/200], Train Loss: 0.0090, Val Loss: 0.0394, Val Balanced Acc: 0.8934, Val F1 Score: 0.9757, Val Acc: 0.9754, Time: 6.05 sec\n",
      "Epoch [36/200], Train Loss: 0.0093, Val Loss: 0.0394, Val Balanced Acc: 0.8789, Val F1 Score: 0.9756, Val Acc: 0.9759, Time: 6.24 sec\n",
      "Epoch [37/200], Train Loss: 0.0060, Val Loss: 0.0409, Val Balanced Acc: 0.8831, Val F1 Score: 0.9758, Val Acc: 0.9761, Time: 6.30 sec\n",
      "Epoch [38/200], Train Loss: 0.0054, Val Loss: 0.0399, Val Balanced Acc: 0.8817, Val F1 Score: 0.9759, Val Acc: 0.9762, Time: 6.00 sec\n",
      "Epoch [39/200], Train Loss: 0.0048, Val Loss: 0.0382, Val Balanced Acc: 0.8947, Val F1 Score: 0.9764, Val Acc: 0.9763, Time: 6.36 sec\n",
      "Epoch [40/200], Train Loss: 0.0058, Val Loss: 0.0382, Val Balanced Acc: 0.8906, Val F1 Score: 0.9762, Val Acc: 0.9763, Time: 6.09 sec\n",
      "Epoch [41/200], Train Loss: 0.0168, Val Loss: 0.0398, Val Balanced Acc: 0.9124, Val F1 Score: 0.9759, Val Acc: 0.9753, Time: 6.23 sec\n",
      "Epoch [42/200], Train Loss: 0.0413, Val Loss: 0.0428, Val Balanced Acc: 0.8495, Val F1 Score: 0.9719, Val Acc: 0.9727, Time: 6.14 sec\n",
      "Epoch [43/200], Train Loss: 0.0073, Val Loss: 0.0401, Val Balanced Acc: 0.8870, Val F1 Score: 0.9759, Val Acc: 0.9761, Time: 6.32 sec\n",
      "Epoch [44/200], Train Loss: 0.1200, Val Loss: 0.0471, Val Balanced Acc: 0.8247, Val F1 Score: 0.9677, Val Acc: 0.9690, Time: 6.27 sec\n",
      "Epoch [45/200], Train Loss: 0.0126, Val Loss: 0.0390, Val Balanced Acc: 0.8913, Val F1 Score: 0.9760, Val Acc: 0.9760, Time: 6.18 sec\n",
      "Epoch [46/200], Train Loss: 0.0064, Val Loss: 0.0387, Val Balanced Acc: 0.8995, Val F1 Score: 0.9754, Val Acc: 0.9750, Time: 6.30 sec\n",
      "Epoch [47/200], Train Loss: 0.0119, Val Loss: 0.0385, Val Balanced Acc: 0.8953, Val F1 Score: 0.9763, Val Acc: 0.9762, Time: 6.16 sec\n",
      "Epoch [48/200], Train Loss: 0.0069, Val Loss: 0.0409, Val Balanced Acc: 0.9008, Val F1 Score: 0.9747, Val Acc: 0.9744, Time: 6.35 sec\n",
      "Epoch [49/200], Train Loss: 0.0062, Val Loss: 0.0398, Val Balanced Acc: 0.8897, Val F1 Score: 0.9755, Val Acc: 0.9755, Time: 6.24 sec\n",
      "Epoch [50/200], Train Loss: 0.0041, Val Loss: 0.0384, Val Balanced Acc: 0.8953, Val F1 Score: 0.9766, Val Acc: 0.9767, Time: 6.17 sec\n",
      "Epoch [51/200], Train Loss: 0.0092, Val Loss: 0.0388, Val Balanced Acc: 0.8914, Val F1 Score: 0.9747, Val Acc: 0.9745, Time: 6.26 sec\n",
      "Epoch [52/200], Train Loss: 0.0137, Val Loss: 0.0461, Val Balanced Acc: 0.8201, Val F1 Score: 0.9706, Val Acc: 0.9727, Time: 6.02 sec\n",
      "Epoch [53/200], Train Loss: 0.0597, Val Loss: 0.0415, Val Balanced Acc: 0.8779, Val F1 Score: 0.9748, Val Acc: 0.9751, Time: 6.30 sec\n",
      "Epoch [54/200], Train Loss: 0.0060, Val Loss: 0.0408, Val Balanced Acc: 0.8848, Val F1 Score: 0.9752, Val Acc: 0.9753, Time: 6.20 sec\n",
      "Epoch [55/200], Train Loss: 0.0142, Val Loss: 0.0489, Val Balanced Acc: 0.8093, Val F1 Score: 0.9697, Val Acc: 0.9725, Time: 6.23 sec\n",
      "Epoch [56/200], Train Loss: 0.0052, Val Loss: 0.0418, Val Balanced Acc: 0.8857, Val F1 Score: 0.9741, Val Acc: 0.9740, Time: 6.22 sec\n",
      "Epoch [57/200], Train Loss: 0.0048, Val Loss: 0.0421, Val Balanced Acc: 0.8919, Val F1 Score: 0.9742, Val Acc: 0.9739, Time: 6.10 sec\n",
      "Epoch [58/200], Train Loss: 0.0043, Val Loss: 0.0425, Val Balanced Acc: 0.8827, Val F1 Score: 0.9744, Val Acc: 0.9745, Time: 6.22 sec\n",
      "Epoch [59/200], Train Loss: 0.0052, Val Loss: 0.0428, Val Balanced Acc: 0.8851, Val F1 Score: 0.9743, Val Acc: 0.9742, Time: 6.18 sec\n",
      "Epoch [60/200], Train Loss: 0.0047, Val Loss: 0.0426, Val Balanced Acc: 0.8910, Val F1 Score: 0.9734, Val Acc: 0.9730, Time: 6.04 sec\n",
      "Epoch [61/200], Train Loss: 0.0041, Val Loss: 0.0430, Val Balanced Acc: 0.9017, Val F1 Score: 0.9717, Val Acc: 0.9706, Time: 6.34 sec\n",
      "Epoch [62/200], Train Loss: 0.0036, Val Loss: 0.0431, Val Balanced Acc: 0.8779, Val F1 Score: 0.9741, Val Acc: 0.9743, Time: 6.16 sec\n",
      "Epoch [63/200], Train Loss: 0.0248, Val Loss: 0.0500, Val Balanced Acc: 0.8725, Val F1 Score: 0.9713, Val Acc: 0.9718, Time: 6.25 sec\n",
      "Epoch [64/200], Train Loss: 0.0104, Val Loss: 0.0414, Val Balanced Acc: 0.8992, Val F1 Score: 0.9743, Val Acc: 0.9737, Time: 6.18 sec\n",
      "Epoch [65/200], Train Loss: 0.0066, Val Loss: 0.0414, Val Balanced Acc: 0.8940, Val F1 Score: 0.9743, Val Acc: 0.9740, Time: 6.23 sec\n",
      "Epoch [66/200], Train Loss: 0.0041, Val Loss: 0.0423, Val Balanced Acc: 0.8894, Val F1 Score: 0.9749, Val Acc: 0.9749, Time: 6.21 sec\n",
      "Epoch [67/200], Train Loss: 0.0049, Val Loss: 0.0424, Val Balanced Acc: 0.8913, Val F1 Score: 0.9755, Val Acc: 0.9755, Time: 6.26 sec\n",
      "Epoch [68/200], Train Loss: 0.0175, Val Loss: 0.0512, Val Balanced Acc: 0.9200, Val F1 Score: 0.9648, Val Acc: 0.9615, Time: 6.18 sec\n",
      "Epoch [69/200], Train Loss: 0.0078, Val Loss: 0.0427, Val Balanced Acc: 0.9091, Val F1 Score: 0.9739, Val Acc: 0.9731, Time: 6.02 sec\n",
      "Epoch [70/200], Train Loss: 0.0042, Val Loss: 0.0423, Val Balanced Acc: 0.8844, Val F1 Score: 0.9762, Val Acc: 0.9766, Time: 6.30 sec\n",
      "Epoch [71/200], Train Loss: 0.0033, Val Loss: 0.0417, Val Balanced Acc: 0.8943, Val F1 Score: 0.9754, Val Acc: 0.9754, Time: 6.18 sec\n",
      "Epoch [72/200], Train Loss: 0.0154, Val Loss: 0.0407, Val Balanced Acc: 0.8729, Val F1 Score: 0.9746, Val Acc: 0.9750, Time: 6.18 sec\n",
      "Epoch [73/200], Train Loss: 0.0169, Val Loss: 0.0410, Val Balanced Acc: 0.9095, Val F1 Score: 0.9743, Val Acc: 0.9735, Time: 6.27 sec\n",
      "Epoch [74/200], Train Loss: 0.0038, Val Loss: 0.0404, Val Balanced Acc: 0.8935, Val F1 Score: 0.9762, Val Acc: 0.9763, Time: 6.11 sec\n",
      "Epoch [75/200], Train Loss: 0.0044, Val Loss: 0.0424, Val Balanced Acc: 0.8792, Val F1 Score: 0.9761, Val Acc: 0.9766, Time: 6.18 sec\n",
      "Epoch [76/200], Train Loss: 0.0050, Val Loss: 0.0400, Val Balanced Acc: 0.8995, Val F1 Score: 0.9756, Val Acc: 0.9755, Time: 6.38 sec\n",
      "Epoch [77/200], Train Loss: 0.0027, Val Loss: 0.0417, Val Balanced Acc: 0.9013, Val F1 Score: 0.9749, Val Acc: 0.9746, Time: 6.26 sec\n",
      "Epoch [78/200], Train Loss: 0.0068, Val Loss: 0.0397, Val Balanced Acc: 0.8888, Val F1 Score: 0.9753, Val Acc: 0.9754, Time: 6.31 sec\n",
      "Epoch [79/200], Train Loss: 0.0031, Val Loss: 0.0398, Val Balanced Acc: 0.8987, Val F1 Score: 0.9756, Val Acc: 0.9754, Time: 6.04 sec\n",
      "Epoch [80/200], Train Loss: 0.0059, Val Loss: 0.0407, Val Balanced Acc: 0.9041, Val F1 Score: 0.9742, Val Acc: 0.9736, Time: 6.33 sec\n",
      "Epoch [81/200], Train Loss: 0.0054, Val Loss: 0.0451, Val Balanced Acc: 0.9101, Val F1 Score: 0.9704, Val Acc: 0.9690, Time: 6.28 sec\n",
      "Epoch [82/200], Train Loss: 0.0028, Val Loss: 0.0411, Val Balanced Acc: 0.8891, Val F1 Score: 0.9744, Val Acc: 0.9743, Time: 6.11 sec\n",
      "Epoch [83/200], Train Loss: 0.0026, Val Loss: 0.0413, Val Balanced Acc: 0.8883, Val F1 Score: 0.9748, Val Acc: 0.9748, Time: 6.46 sec\n",
      "Epoch [84/200], Train Loss: 0.0037, Val Loss: 0.0402, Val Balanced Acc: 0.8894, Val F1 Score: 0.9747, Val Acc: 0.9747, Time: 6.09 sec\n",
      "Epoch [85/200], Train Loss: 0.0034, Val Loss: 0.0419, Val Balanced Acc: 0.9058, Val F1 Score: 0.9733, Val Acc: 0.9727, Time: 6.25 sec\n",
      "Epoch [86/200], Train Loss: 0.0041, Val Loss: 0.0406, Val Balanced Acc: 0.9027, Val F1 Score: 0.9744, Val Acc: 0.9739, Time: 6.26 sec\n",
      "Epoch [87/200], Train Loss: 0.0209, Val Loss: 0.0418, Val Balanced Acc: 0.8861, Val F1 Score: 0.9737, Val Acc: 0.9736, Time: 6.20 sec\n",
      "Epoch [88/200], Train Loss: 0.0039, Val Loss: 0.0405, Val Balanced Acc: 0.8897, Val F1 Score: 0.9737, Val Acc: 0.9734, Time: 6.19 sec\n",
      "Epoch [89/200], Train Loss: 0.0159, Val Loss: 0.0370, Val Balanced Acc: 0.8983, Val F1 Score: 0.9751, Val Acc: 0.9747, Time: 6.17 sec\n",
      "Epoch [90/200], Train Loss: 0.0026, Val Loss: 0.0358, Val Balanced Acc: 0.8952, Val F1 Score: 0.9760, Val Acc: 0.9759, Time: 6.24 sec\n",
      "Epoch [91/200], Train Loss: 0.0030, Val Loss: 0.0388, Val Balanced Acc: 0.9047, Val F1 Score: 0.9725, Val Acc: 0.9716, Time: 6.13 sec\n",
      "Epoch [92/200], Train Loss: 0.0037, Val Loss: 0.0382, Val Balanced Acc: 0.8970, Val F1 Score: 0.9728, Val Acc: 0.9722, Time: 6.29 sec\n",
      "Epoch [93/200], Train Loss: 0.0036, Val Loss: 0.0423, Val Balanced Acc: 0.8575, Val F1 Score: 0.9704, Val Acc: 0.9706, Time: 6.28 sec\n",
      "Epoch [94/200], Train Loss: 0.0055, Val Loss: 0.0369, Val Balanced Acc: 0.9040, Val F1 Score: 0.9749, Val Acc: 0.9744, Time: 6.23 sec\n",
      "Epoch [95/200], Train Loss: 0.0022, Val Loss: 0.0389, Val Balanced Acc: 0.9138, Val F1 Score: 0.9711, Val Acc: 0.9697, Time: 6.19 sec\n",
      "Epoch [96/200], Train Loss: 0.0025, Val Loss: 0.0397, Val Balanced Acc: 0.8973, Val F1 Score: 0.9747, Val Acc: 0.9746, Time: 6.09 sec\n",
      "Epoch [97/200], Train Loss: 0.0025, Val Loss: 0.0389, Val Balanced Acc: 0.8662, Val F1 Score: 0.9740, Val Acc: 0.9746, Time: 6.27 sec\n",
      "Epoch [98/200], Train Loss: 0.0221, Val Loss: 0.0391, Val Balanced Acc: 0.9111, Val F1 Score: 0.9747, Val Acc: 0.9739, Time: 6.23 sec\n",
      "Epoch [99/200], Train Loss: 0.0065, Val Loss: 0.0395, Val Balanced Acc: 0.9021, Val F1 Score: 0.9754, Val Acc: 0.9752, Time: 6.23 sec\n",
      "Epoch [100/200], Train Loss: 0.0028, Val Loss: 0.0378, Val Balanced Acc: 0.9043, Val F1 Score: 0.9744, Val Acc: 0.9738, Time: 6.23 sec\n",
      "Epoch [101/200], Train Loss: 0.0027, Val Loss: 0.0407, Val Balanced Acc: 0.9029, Val F1 Score: 0.9739, Val Acc: 0.9734, Time: 6.06 sec\n",
      "Epoch [102/200], Train Loss: 0.0031, Val Loss: 0.0403, Val Balanced Acc: 0.9026, Val F1 Score: 0.9713, Val Acc: 0.9701, Time: 6.37 sec\n",
      "Epoch [103/200], Train Loss: 0.0034, Val Loss: 0.0409, Val Balanced Acc: 0.9068, Val F1 Score: 0.9743, Val Acc: 0.9738, Time: 6.25 sec\n",
      "Epoch [104/200], Train Loss: 0.0022, Val Loss: 0.0398, Val Balanced Acc: 0.8888, Val F1 Score: 0.9754, Val Acc: 0.9756, Time: 6.09 sec\n",
      "Epoch [105/200], Train Loss: 0.0024, Val Loss: 0.0387, Val Balanced Acc: 0.9044, Val F1 Score: 0.9747, Val Acc: 0.9743, Time: 6.39 sec\n",
      "Epoch [106/200], Train Loss: 0.0021, Val Loss: 0.0405, Val Balanced Acc: 0.9075, Val F1 Score: 0.9727, Val Acc: 0.9718, Time: 6.14 sec\n",
      "Epoch [107/200], Train Loss: 0.0038, Val Loss: 0.0424, Val Balanced Acc: 0.9060, Val F1 Score: 0.9727, Val Acc: 0.9719, Time: 6.29 sec\n",
      "Epoch [108/200], Train Loss: 0.0055, Val Loss: 0.0427, Val Balanced Acc: 0.8993, Val F1 Score: 0.9743, Val Acc: 0.9738, Time: 6.24 sec\n",
      "Epoch [109/200], Train Loss: 0.0024, Val Loss: 0.0418, Val Balanced Acc: 0.9013, Val F1 Score: 0.9753, Val Acc: 0.9750, Time: 6.18 sec\n",
      "Epoch [110/200], Train Loss: 0.0024, Val Loss: 0.0422, Val Balanced Acc: 0.8934, Val F1 Score: 0.9755, Val Acc: 0.9756, Time: 6.24 sec\n",
      "Epoch [111/200], Train Loss: 0.0019, Val Loss: 0.0437, Val Balanced Acc: 0.8980, Val F1 Score: 0.9735, Val Acc: 0.9731, Time: 6.30 sec\n",
      "Epoch [112/200], Train Loss: 0.0020, Val Loss: 0.0412, Val Balanced Acc: 0.8966, Val F1 Score: 0.9747, Val Acc: 0.9745, Time: 6.24 sec\n",
      "Epoch [113/200], Train Loss: 0.0017, Val Loss: 0.0416, Val Balanced Acc: 0.9020, Val F1 Score: 0.9742, Val Acc: 0.9737, Time: 6.09 sec\n",
      "Epoch [114/200], Train Loss: 0.0015, Val Loss: 0.0413, Val Balanced Acc: 0.8972, Val F1 Score: 0.9738, Val Acc: 0.9735, Time: 6.23 sec\n",
      "Epoch [115/200], Train Loss: 0.0017, Val Loss: 0.0430, Val Balanced Acc: 0.8961, Val F1 Score: 0.9728, Val Acc: 0.9723, Time: 6.30 sec\n",
      "Epoch [116/200], Train Loss: 0.0084, Val Loss: 0.0408, Val Balanced Acc: 0.8887, Val F1 Score: 0.9735, Val Acc: 0.9733, Time: 6.20 sec\n",
      "Epoch [117/200], Train Loss: 0.0033, Val Loss: 0.0441, Val Balanced Acc: 0.9036, Val F1 Score: 0.9712, Val Acc: 0.9702, Time: 6.24 sec\n",
      "Epoch [118/200], Train Loss: 0.0220, Val Loss: 0.0730, Val Balanced Acc: 0.7563, Val F1 Score: 0.9612, Val Acc: 0.9658, Time: 6.12 sec\n",
      "Epoch [119/200], Train Loss: 0.0071, Val Loss: 0.0557, Val Balanced Acc: 0.8673, Val F1 Score: 0.9744, Val Acc: 0.9750, Time: 6.20 sec\n",
      "Epoch [120/200], Train Loss: 0.0033, Val Loss: 0.0507, Val Balanced Acc: 0.8784, Val F1 Score: 0.9735, Val Acc: 0.9737, Time: 6.23 sec\n",
      "Epoch [121/200], Train Loss: 0.0022, Val Loss: 0.0499, Val Balanced Acc: 0.8968, Val F1 Score: 0.9731, Val Acc: 0.9725, Time: 6.22 sec\n",
      "Epoch [122/200], Train Loss: 0.0038, Val Loss: 0.0456, Val Balanced Acc: 0.8821, Val F1 Score: 0.9743, Val Acc: 0.9743, Time: 6.26 sec\n",
      "Epoch [123/200], Train Loss: 0.0019, Val Loss: 0.0460, Val Balanced Acc: 0.8957, Val F1 Score: 0.9738, Val Acc: 0.9735, Time: 6.01 sec\n",
      "Epoch [124/200], Train Loss: 0.0017, Val Loss: 0.0438, Val Balanced Acc: 0.8998, Val F1 Score: 0.9743, Val Acc: 0.9740, Time: 6.30 sec\n",
      "Epoch [125/200], Train Loss: 0.0023, Val Loss: 0.0482, Val Balanced Acc: 0.9051, Val F1 Score: 0.9695, Val Acc: 0.9681, Time: 6.23 sec\n",
      "Epoch [126/200], Train Loss: 0.0021, Val Loss: 0.0463, Val Balanced Acc: 0.8837, Val F1 Score: 0.9768, Val Acc: 0.9773, Time: 6.06 sec\n",
      "Epoch [127/200], Train Loss: 0.0016, Val Loss: 0.0445, Val Balanced Acc: 0.8961, Val F1 Score: 0.9754, Val Acc: 0.9754, Time: 6.49 sec\n",
      "Epoch [128/200], Train Loss: 0.0014, Val Loss: 0.0426, Val Balanced Acc: 0.9066, Val F1 Score: 0.9735, Val Acc: 0.9729, Time: 6.13 sec\n",
      "Epoch [129/200], Train Loss: 0.0014, Val Loss: 0.0447, Val Balanced Acc: 0.8997, Val F1 Score: 0.9745, Val Acc: 0.9743, Time: 6.27 sec\n",
      "Epoch [130/200], Train Loss: 0.0022, Val Loss: 0.0471, Val Balanced Acc: 0.8949, Val F1 Score: 0.9723, Val Acc: 0.9719, Time: 6.18 sec\n",
      "Epoch [131/200], Train Loss: 0.0015, Val Loss: 0.0464, Val Balanced Acc: 0.9066, Val F1 Score: 0.9686, Val Acc: 0.9670, Time: 6.28 sec\n",
      "Epoch [132/200], Train Loss: 0.0017, Val Loss: 0.0446, Val Balanced Acc: 0.8974, Val F1 Score: 0.9717, Val Acc: 0.9711, Time: 6.20 sec\n",
      "Epoch [133/200], Train Loss: 0.0013, Val Loss: 0.0455, Val Balanced Acc: 0.8977, Val F1 Score: 0.9718, Val Acc: 0.9712, Time: 6.16 sec\n",
      "Epoch [134/200], Train Loss: 0.0013, Val Loss: 0.0432, Val Balanced Acc: 0.8992, Val F1 Score: 0.9738, Val Acc: 0.9734, Time: 6.27 sec\n",
      "Epoch [135/200], Train Loss: 0.0013, Val Loss: 0.0413, Val Balanced Acc: 0.9017, Val F1 Score: 0.9736, Val Acc: 0.9731, Time: 6.10 sec\n",
      "Epoch [136/200], Train Loss: 0.0013, Val Loss: 0.0440, Val Balanced Acc: 0.9059, Val F1 Score: 0.9708, Val Acc: 0.9698, Time: 6.26 sec\n",
      "Epoch [137/200], Train Loss: 0.0027, Val Loss: 0.0428, Val Balanced Acc: 0.8952, Val F1 Score: 0.9743, Val Acc: 0.9743, Time: 6.20 sec\n",
      "Epoch [138/200], Train Loss: 0.0022, Val Loss: 0.0395, Val Balanced Acc: 0.9077, Val F1 Score: 0.9750, Val Acc: 0.9746, Time: 6.26 sec\n",
      "Epoch [139/200], Train Loss: 0.0072, Val Loss: 0.0453, Val Balanced Acc: 0.9143, Val F1 Score: 0.9679, Val Acc: 0.9663, Time: 6.21 sec\n",
      "Epoch [140/200], Train Loss: 0.0016, Val Loss: 0.0423, Val Balanced Acc: 0.8986, Val F1 Score: 0.9726, Val Acc: 0.9721, Time: 6.05 sec\n",
      "Epoch [141/200], Train Loss: 0.0015, Val Loss: 0.0427, Val Balanced Acc: 0.9011, Val F1 Score: 0.9709, Val Acc: 0.9702, Time: 6.24 sec\n",
      "Epoch [142/200], Train Loss: 0.0013, Val Loss: 0.0449, Val Balanced Acc: 0.9025, Val F1 Score: 0.9699, Val Acc: 0.9690, Time: 6.23 sec\n",
      "Epoch [143/200], Train Loss: 0.0014, Val Loss: 0.0486, Val Balanced Acc: 0.9041, Val F1 Score: 0.9671, Val Acc: 0.9656, Time: 6.27 sec\n",
      "Epoch [144/200], Train Loss: 0.0019, Val Loss: 0.0470, Val Balanced Acc: 0.8884, Val F1 Score: 0.9712, Val Acc: 0.9709, Time: 6.25 sec\n",
      "Epoch [145/200], Train Loss: 0.0026, Val Loss: 0.0397, Val Balanced Acc: 0.8862, Val F1 Score: 0.9729, Val Acc: 0.9728, Time: 6.14 sec\n",
      "Epoch [146/200], Train Loss: 0.0084, Val Loss: 0.0435, Val Balanced Acc: 0.8904, Val F1 Score: 0.9747, Val Acc: 0.9748, Time: 6.32 sec\n",
      "Epoch [147/200], Train Loss: 0.0084, Val Loss: 0.0572, Val Balanced Acc: 0.8078, Val F1 Score: 0.9650, Val Acc: 0.9672, Time: 6.32 sec\n",
      "Epoch [148/200], Train Loss: 0.0023, Val Loss: 0.0508, Val Balanced Acc: 0.8904, Val F1 Score: 0.9697, Val Acc: 0.9690, Time: 6.05 sec\n",
      "Epoch [149/200], Train Loss: 0.0066, Val Loss: 0.0520, Val Balanced Acc: 0.8355, Val F1 Score: 0.9702, Val Acc: 0.9718, Time: 6.49 sec\n",
      "Epoch [150/200], Train Loss: 0.0025, Val Loss: 0.0485, Val Balanced Acc: 0.8805, Val F1 Score: 0.9720, Val Acc: 0.9720, Time: 6.11 sec\n",
      "Epoch [151/200], Train Loss: 0.0013, Val Loss: 0.0472, Val Balanced Acc: 0.8953, Val F1 Score: 0.9702, Val Acc: 0.9696, Time: 6.30 sec\n",
      "Epoch [152/200], Train Loss: 0.0012, Val Loss: 0.0472, Val Balanced Acc: 0.9025, Val F1 Score: 0.9701, Val Acc: 0.9692, Time: 6.21 sec\n",
      "Epoch [153/200], Train Loss: 0.0010, Val Loss: 0.0456, Val Balanced Acc: 0.8976, Val F1 Score: 0.9721, Val Acc: 0.9717, Time: 6.26 sec\n",
      "Epoch [154/200], Train Loss: 0.0010, Val Loss: 0.0484, Val Balanced Acc: 0.8965, Val F1 Score: 0.9712, Val Acc: 0.9707, Time: 6.22 sec\n",
      "Epoch [155/200], Train Loss: 0.0015, Val Loss: 0.0457, Val Balanced Acc: 0.8974, Val F1 Score: 0.9697, Val Acc: 0.9688, Time: 6.23 sec\n",
      "Epoch [156/200], Train Loss: 0.0027, Val Loss: 0.0472, Val Balanced Acc: 0.8975, Val F1 Score: 0.9725, Val Acc: 0.9721, Time: 6.21 sec\n",
      "Epoch [157/200], Train Loss: 0.0011, Val Loss: 0.0479, Val Balanced Acc: 0.8924, Val F1 Score: 0.9700, Val Acc: 0.9695, Time: 6.07 sec\n",
      "Epoch [158/200], Train Loss: 0.0011, Val Loss: 0.0463, Val Balanced Acc: 0.9003, Val F1 Score: 0.9713, Val Acc: 0.9707, Time: 6.24 sec\n",
      "Epoch [159/200], Train Loss: 0.0008, Val Loss: 0.0454, Val Balanced Acc: 0.9029, Val F1 Score: 0.9708, Val Acc: 0.9700, Time: 6.26 sec\n",
      "Epoch [160/200], Train Loss: 0.0009, Val Loss: 0.0493, Val Balanced Acc: 0.9010, Val F1 Score: 0.9686, Val Acc: 0.9674, Time: 6.29 sec\n",
      "Epoch [161/200], Train Loss: 0.0012, Val Loss: 0.0473, Val Balanced Acc: 0.9008, Val F1 Score: 0.9676, Val Acc: 0.9663, Time: 6.15 sec\n",
      "Epoch [162/200], Train Loss: 0.0035, Val Loss: 0.0470, Val Balanced Acc: 0.8896, Val F1 Score: 0.9718, Val Acc: 0.9716, Time: 6.09 sec\n",
      "Epoch [163/200], Train Loss: 0.0016, Val Loss: 0.0480, Val Balanced Acc: 0.9054, Val F1 Score: 0.9689, Val Acc: 0.9676, Time: 6.21 sec\n",
      "Epoch [164/200], Train Loss: 0.0019, Val Loss: 0.0464, Val Balanced Acc: 0.9073, Val F1 Score: 0.9675, Val Acc: 0.9656, Time: 6.22 sec\n",
      "Epoch [165/200], Train Loss: 0.0039, Val Loss: 0.0537, Val Balanced Acc: 0.8943, Val F1 Score: 0.9709, Val Acc: 0.9704, Time: 6.34 sec\n",
      "Epoch [166/200], Train Loss: 0.0012, Val Loss: 0.0534, Val Balanced Acc: 0.9020, Val F1 Score: 0.9680, Val Acc: 0.9670, Time: 6.28 sec\n",
      "Epoch [167/200], Train Loss: 0.0010, Val Loss: 0.0526, Val Balanced Acc: 0.8995, Val F1 Score: 0.9671, Val Acc: 0.9658, Time: 6.08 sec\n",
      "Epoch [168/200], Train Loss: 0.0011, Val Loss: 0.0514, Val Balanced Acc: 0.8957, Val F1 Score: 0.9681, Val Acc: 0.9673, Time: 6.18 sec\n",
      "Epoch [169/200], Train Loss: 0.0010, Val Loss: 0.0527, Val Balanced Acc: 0.8995, Val F1 Score: 0.9677, Val Acc: 0.9667, Time: 6.36 sec\n",
      "Epoch [170/200], Train Loss: 0.0010, Val Loss: 0.0512, Val Balanced Acc: 0.8942, Val F1 Score: 0.9693, Val Acc: 0.9687, Time: 6.16 sec\n",
      "Epoch [171/200], Train Loss: 0.0008, Val Loss: 0.0507, Val Balanced Acc: 0.9077, Val F1 Score: 0.9671, Val Acc: 0.9656, Time: 6.42 sec\n",
      "Epoch [172/200], Train Loss: 0.0008, Val Loss: 0.0575, Val Balanced Acc: 0.9022, Val F1 Score: 0.9637, Val Acc: 0.9620, Time: 6.12 sec\n",
      "Epoch [173/200], Train Loss: 0.0008, Val Loss: 0.0479, Val Balanced Acc: 0.9024, Val F1 Score: 0.9701, Val Acc: 0.9693, Time: 6.28 sec\n",
      "Epoch [174/200], Train Loss: 0.0009, Val Loss: 0.0507, Val Balanced Acc: 0.9112, Val F1 Score: 0.9681, Val Acc: 0.9666, Time: 6.14 sec\n",
      "Epoch [175/200], Train Loss: 0.0008, Val Loss: 0.0514, Val Balanced Acc: 0.8996, Val F1 Score: 0.9707, Val Acc: 0.9700, Time: 6.27 sec\n",
      "Epoch [176/200], Train Loss: 0.0009, Val Loss: 0.0512, Val Balanced Acc: 0.8875, Val F1 Score: 0.9694, Val Acc: 0.9690, Time: 6.13 sec\n",
      "Epoch [177/200], Train Loss: 0.0008, Val Loss: 0.0490, Val Balanced Acc: 0.8974, Val F1 Score: 0.9700, Val Acc: 0.9694, Time: 6.30 sec\n",
      "Epoch [178/200], Train Loss: 0.0007, Val Loss: 0.0560, Val Balanced Acc: 0.9076, Val F1 Score: 0.9645, Val Acc: 0.9629, Time: 6.26 sec\n",
      "Epoch [179/200], Train Loss: 0.0007, Val Loss: 0.0504, Val Balanced Acc: 0.9109, Val F1 Score: 0.9691, Val Acc: 0.9677, Time: 5.99 sec\n",
      "Epoch [180/200], Train Loss: 0.0007, Val Loss: 0.0575, Val Balanced Acc: 0.8967, Val F1 Score: 0.9643, Val Acc: 0.9629, Time: 6.17 sec\n",
      "Epoch [181/200], Train Loss: 0.0008, Val Loss: 0.0532, Val Balanced Acc: 0.9019, Val F1 Score: 0.9674, Val Acc: 0.9663, Time: 6.27 sec\n",
      "Epoch [182/200], Train Loss: 0.0008, Val Loss: 0.0587, Val Balanced Acc: 0.9156, Val F1 Score: 0.9621, Val Acc: 0.9592, Time: 6.22 sec\n",
      "Epoch [183/200], Train Loss: 0.0007, Val Loss: 0.0517, Val Balanced Acc: 0.8807, Val F1 Score: 0.9711, Val Acc: 0.9711, Time: 6.20 sec\n",
      "Epoch [184/200], Train Loss: 0.0007, Val Loss: 0.0533, Val Balanced Acc: 0.9040, Val F1 Score: 0.9679, Val Acc: 0.9667, Time: 6.17 sec\n",
      "Epoch [185/200], Train Loss: 0.0007, Val Loss: 0.0533, Val Balanced Acc: 0.9057, Val F1 Score: 0.9694, Val Acc: 0.9684, Time: 6.25 sec\n",
      "Epoch [186/200], Train Loss: 0.0007, Val Loss: 0.0535, Val Balanced Acc: 0.8872, Val F1 Score: 0.9685, Val Acc: 0.9681, Time: 6.25 sec\n",
      "Epoch [187/200], Train Loss: 0.0007, Val Loss: 0.0552, Val Balanced Acc: 0.8958, Val F1 Score: 0.9686, Val Acc: 0.9678, Time: 6.27 sec\n",
      "Epoch [188/200], Train Loss: 0.0007, Val Loss: 0.0587, Val Balanced Acc: 0.8993, Val F1 Score: 0.9663, Val Acc: 0.9648, Time: 6.25 sec\n",
      "Epoch [189/200], Train Loss: 0.0007, Val Loss: 0.0583, Val Balanced Acc: 0.9023, Val F1 Score: 0.9669, Val Acc: 0.9655, Time: 6.13 sec\n",
      "Epoch [190/200], Train Loss: 0.0006, Val Loss: 0.0640, Val Balanced Acc: 0.9027, Val F1 Score: 0.9629, Val Acc: 0.9607, Time: 6.16 sec\n",
      "Epoch [191/200], Train Loss: 0.0005, Val Loss: 0.0564, Val Balanced Acc: 0.8959, Val F1 Score: 0.9693, Val Acc: 0.9686, Time: 6.22 sec\n",
      "Epoch [192/200], Train Loss: 0.0006, Val Loss: 0.0515, Val Balanced Acc: 0.8866, Val F1 Score: 0.9718, Val Acc: 0.9717, Time: 6.17 sec\n",
      "Epoch [193/200], Train Loss: 0.0006, Val Loss: 0.0529, Val Balanced Acc: 0.8973, Val F1 Score: 0.9707, Val Acc: 0.9700, Time: 6.34 sec\n",
      "Epoch [194/200], Train Loss: 0.0006, Val Loss: 0.0608, Val Balanced Acc: 0.8993, Val F1 Score: 0.9636, Val Acc: 0.9620, Time: 6.16 sec\n",
      "Epoch [195/200], Train Loss: 0.0008, Val Loss: 0.0532, Val Balanced Acc: 0.9157, Val F1 Score: 0.9659, Val Acc: 0.9638, Time: 6.28 sec\n",
      "Epoch [196/200], Train Loss: 0.0005, Val Loss: 0.0593, Val Balanced Acc: 0.9103, Val F1 Score: 0.9645, Val Acc: 0.9624, Time: 6.28 sec\n",
      "Epoch [197/200], Train Loss: 0.0037, Val Loss: 0.0556, Val Balanced Acc: 0.9011, Val F1 Score: 0.9684, Val Acc: 0.9668, Time: 6.26 sec\n",
      "Epoch [198/200], Train Loss: 0.0139, Val Loss: 0.0552, Val Balanced Acc: 0.8671, Val F1 Score: 0.9674, Val Acc: 0.9671, Time: 6.27 sec\n",
      "Epoch [199/200], Train Loss: 0.0023, Val Loss: 0.0544, Val Balanced Acc: 0.8995, Val F1 Score: 0.9707, Val Acc: 0.9698, Time: 6.27 sec\n",
      "Epoch [200/200], Train Loss: 0.0015, Val Loss: 0.0637, Val Balanced Acc: 0.8618, Val F1 Score: 0.9630, Val Acc: 0.9622, Time: 6.21 sec\n"
     ]
    }
   ],
   "source": [
    "n_features = 5\n",
    "model = CNN2D(n_features=n_features)\n",
    "batch_size = 256\n",
    "lr = 1E-4\n",
    "criterion = FocalLoss()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n",
    "epochs = 200\n",
    "steps_per_epoch = 100\n",
    "device = 'cuda'\n",
    "model_id = datetime.datetime.now().strftime(\"%Y%m%d%H%M\")\n",
    "checkpoint_path = Path('models') / model_id\n",
    "\n",
    "sampler = BalancedBatchSampler(y_test, 3, batch_size)\n",
    "\n",
    "train_dataset = TensorDataset(\n",
    "    torch.from_numpy(X_cwt_train),\n",
    "    torch.from_numpy(rr_train),\n",
    "    torch.from_numpy(X_feat_train[:, :n_features]),\n",
    "    torch.from_numpy(y_train)\n",
    ")\n",
    "val_dataset = TensorDataset(\n",
    "    torch.from_numpy(X_cwt_test),\n",
    "    torch.from_numpy(rr_test),\n",
    "    torch.from_numpy(X_feat_test[:, :n_features]),\n",
    "    torch.from_numpy(y_test)\n",
    ")\n",
    "train_loader = DataLoader(train_dataset, batch_sampler=sampler)\n",
    "val_loader = DataLoader(val_dataset, batch_size=4096, shuffle=False)\n",
    "\n",
    "writer = SummaryWriter(log_dir='logs')\n",
    "best_val_bal_acc = 0.0\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    model.train()\n",
    "    start_time = time()\n",
    "    running_loss = 0.0\n",
    "\n",
    "    training_iterator = iter(train_loader)\n",
    "    for i in range(steps_per_epoch):\n",
    "        X, X_rr, X_feat, y = next(training_iterator)\n",
    "        X, X_rr, X_feat, y = X.to(device), X_rr.to(device), X_feat.to(device), y.to(device)\n",
    "\n",
    "        outputs = model(X, X_rr, X_feat)\n",
    "        loss = criterion(outputs, y)\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        running_loss += loss.item()\n",
    "\n",
    "    avg_train_loss = running_loss / steps_per_epoch\n",
    "\n",
    "    model.eval()\n",
    "    val_running_loss = 0.0\n",
    "    all_preds = []\n",
    "    all_targets = []\n",
    "    with torch.no_grad():\n",
    "        for X, X_rr, X_feat, y in val_loader:\n",
    "            X, X_rr, X_feat, y = X.to(device), X_rr.to(device), X_feat.to(device), y.to(device)\n",
    "            \n",
    "            outputs = model(X, X_rr, X_feat)\n",
    "            loss = criterion(outputs, y)\n",
    "            val_running_loss += loss.item()\n",
    "\n",
    "            preds = torch.argmax(outputs, dim=1)\n",
    "            all_preds.extend(preds.cpu().numpy())\n",
    "            all_targets.extend(y.cpu().numpy())\n",
    "\n",
    "    avg_val_loss = val_running_loss / len(val_loader)\n",
    "    val_bal_acc = balanced_accuracy_score(all_targets, all_preds)\n",
    "    val_f1_score = f1_score(all_targets, all_preds, average='weighted')\n",
    "    val_acc = accuracy_score(all_targets, all_preds)\n",
    "\n",
    "    end_time = time()\n",
    "    epoch_time = end_time - start_time\n",
    "\n",
    "    writer.add_scalar('Loss/Train', avg_train_loss, epoch)\n",
    "    writer.add_scalar('Loss/Val', avg_val_loss, epoch)\n",
    "    writer.add_scalar('Accuracy/Val_Balanced', val_bal_acc, epoch)\n",
    "    writer.add_scalar('F1_Score/Val', val_f1_score, epoch)\n",
    "    writer.add_scalar('Accuracy/Val', val_acc, epoch)\n",
    "    writer.add_scalar('Time/Epoch', epoch_time, epoch)\n",
    "    \n",
    "    if val_bal_acc > best_val_bal_acc:\n",
    "        best_val_bal_acc = val_bal_acc\n",
    "        torch.save(model.state_dict(), checkpoint_path)\n",
    "\n",
    "    print(f'Epoch [{epoch+1}/{epochs}], Train Loss: {avg_train_loss:.4f}, Val Loss: {avg_val_loss:.4f}, Val Balanced Acc: {val_bal_acc:.4f}, Val F1 Score: {val_f1_score:.4f}, Val Acc: {val_acc:.4f}, Time: {epoch_time:.2f} sec')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "dc1efd66-97a0-4f8d-b8a1-5102aebdcff1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QAgAYg9ACABiD0AIAGIPQAgAYg9ACABiD0AIAGIPQAgAYg9ACABiD0AIAGIPQAgAYg9ACABiD0AIAGIPQAgAYg9ACABiD0AIAGIPQAgAYg9ACABiD0AIAGIPQAgAYg9ACABiD0AIAGIPQAgAYg9ACABiD0AIAGIPQAgAYg9ACABiD0AIAGIPQAgAYg9ACABiD0AIAGIPQAgAYg9ACABiD0AIAGIPQAgAYw+7pAmrSbf/XS3Zvh6fLQC27Z9NyT5eAOjT/6gRPl4BaZjlLpMwza8uZFgDAGIQWAMAYhBYAwBiEFgDAGIQWAMAYhBYAwBiEFgDAGIQWAMAYhBYAwBiEFgDAGIQWAMAYhBYAwBiEFgDAGIQWAMAYhBYAwBiEFgDAGIQWAMAYhBYAwBiEFgDAGIQWAMAYhBYAwBiEFgDAGIQWAMAYhBYAwBiEFgDAGIQWAMAYhBYAwBiEFgDAGIQWAMAYhBYAwBj2M2n0ySefnPEKb7rpprMuBgCAUzmj0Lr55pvPaGU2m03l5eW/pR4AAKp0RqHldDpruw4AAE7rN13TOnr0aE3VAQDAaVU7tMrLyzVp0iQ1btxYgYGBSk9PlySNGTNGb7/9do0XCADAr6odWs8884zmzJmjadOmydfX1zX9kksu0cyZM2u0OAAAjlft0Jo3b57efPNN9e/fX97e3q7pHTp00ObNm2u0OAAAjlft0Nq7d6/i4uJOmu50OlVaWlojRQEAUJlqh1bbtm2VnJx80vSPPvpIHTt2rJGiAACozBkNeT/e2LFjNXDgQO3du1dOp1OLFi3Sli1bNG/ePH366ae1USMAAJLO4kyrd+/eWrJkif7973+rXr16Gjt2rFJSUrRkyRJdd911tVEjAACSzuJMS5K6dOmiZcuW1XQtAACc0lmFliStWbNGKSkpkiquc3Xu3LnGigIAoDLVDq09e/bozjvv1MqVKxUaGipJysnJ0VVXXaX33ntPTZo0qekaAQCQdBbXtIYMGaLS0lKlpKQoKytLWVlZSklJkdPp1JAhQ2qjRgAAJJ3Fmdby5cu1atUqxcfHu6bFx8fr5ZdfVpcuXWq0OAAAjlftM62YmJhKP0RcXl6u6OjoGikKAIDKVDu0nnvuOT388MNas2aNa9qaNWs0YsQI/e1vf6vR4gAAON4ZdQ+GhYXJZrO57h85ckRXXHGF7PaKxcvKymS32zVo0KAz/sFIAACq64xC68UXX6zlMgAAOL0zCq2BAwfWdh0AAJzWWX+4WKr45eKSkhK3acHBwb+pIAAAqlLtgRhHjhzRQw89pAYNGqhevXoKCwtzuwEAUFuqHVqPP/64vvrqK7322mtyOByaOXOmJkyYoOjoaM2bN682agQAQNJZdA8uWbJE8+bN0zXXXKN7771XXbp0UVxcnJo1a6b58+erf//+tVEnAADVP9PKyspSbGyspIrrV1lZWZKk3//+9/r2229rtjoAAI5T7TOt2NhYbd++XU2bNlXr1q31wQcf6PLLL9eSJUtcX6CLs3Pjzenq02+bwsKPantaiF57qb22bg6vtG3T5nm6Z1CK4lrlqGGjQr3xcjv986M4tzaXtD+sPnduU1yrHNWPOKpJT1+h1Sv41pJzweb5gfrp7SAVZXgrvHWJLh+To4j2JVW2/3lOoLa+G6gj+73lCHOqWWKROj2aI29HxfwtC+ppy7uBOrK34iUd0rJUHR7IU+NuR+tic3CcG+/Yoz5JuxQWUaLtWwP12uRW2vpj1QPUfn/dId3zULoaRh/Vvl3+mvXCRVqzIsI1f+Skn3Vd7wNuy6xZGa6xwxJc9xs3K9SgR1LVNiFXPj5Obd8aqHdejdWm/51/4wyqfaZ17733auPGjZKkJ598Uq+++qr8/Pw0cuRIPfbYY2dVREZGhoYNG6amTZvK4XAoKipKiYmJWrly5Vmtz0Rdu+/R0Ad/0IK5rfXw0O5KTwvRpL+tUkhocaXtHX7l2r8vQLPfvFhZmY5K2/j5l2l7aohmvNihNktHNW1f6q81k0PV4cE83fjxAYW1LtW/B0eqKLPyl2P6kgCtez5U7R/KU++lB3TVM1nasTRA6/4e6moTEFWuTqNydcOig7ph4UE1+l2xvn4wQjnbftMAYVRT18SDGvrYNi14vbkevuMypW8J1KTXNygkvPIDkjYdcvXE1J/05ceN9HDfy7T6q0iNeekHNYsrcGu3ZkW4+ne/2nWb9vjFbvPHv7xR3t6WRg/pqOH9LtP2rYEa/8pGhdWv/P3DZNV+Ro8cOdL1/x49emjz5s1au3at4uLi1L59+7Mqok+fPiopKdHcuXMVGxurgwcP6j//+Y8yMzPPan0muqVvqj7/tLmWfdZMkvTK8wm67HcHdP0fd+jDBfEntd+2OUzbNlccRd1730+VrnPN91Fa831U7RWNs5IyO0gt+xYors8RSdLvJmRrzzd+Sl1YT+3uyz+pfcZ6XzXoVKzYXoWSpMAm5WpxY6EyNvq62sT8wf2MquPIXG15t54yNjgU2rKsFrcGx7tlwG59vjBay/5Z0aPxyqR4XdblsK6/eZ8+nNX8pPa9++/W2pXhWjin4nX/zqux6nhllnr126NX/tra1a60xEvZVRycBoeWqHHzIr04vo12bAuUJM1+8SLd2G+vmsUdqXI5U/3mw7BmzZqpWbNmZ718Tk6OkpOT9c0336hbt26udV5++eW/tTRj2O1OxbXK0QfzW7mmWZZNG9ZGqvXFWR6sDDWtvETK/MlXl/zpWDjZvKRGVxUrY71D0smhFdmxROmf1NPhTb6KaF+i/N3e2rvcT7G9j1T6GM5yaefn/ior9FJkx/PvSPtcZbc7FdcmXx/MPPZ+aFk2bfg+XK075FW6TOsOufr4naZu09auCteV3Q+7TWt3aY4WfJOsgjwfbfxvmOa9HKv8XB9JUl6Oj3ZvD9C1vfYrNSVIpSU2/d/t+5Sd6aPUn4NqeCs974xCa/r06We8wuHDh1ergMDAQAUGBmrx4sX63e9+J4fj9EcFxcXFKi4+9mLMy6v8CWGK4JBiedstZWe7b3tOtp9imhZUsRRMVJztJavcJv/65W7T/euXKy+98pdjbK9CFWd76fO7GsiyJKvMplb9CtTufveAy97io8/6NVB5sU32AEvXvHpYoXGcZdWV4LDSitdxpq/b9JxMX8W0KKx0mbCIEuVk+pzUPizi2Pvb2pX1teo/kTq411+NmhRp4PA0TZyxQY/ec6mcTpskm566L0FjX/xBC1cvl+W0KSfLR2OGJagg30fnmzMKrRdeeOGMVmaz2aodWna7XXPmzNHQoUP1+uuvq1OnTurWrZv69etXZXfj5MmTNWHChGo9DmCqA9879MMbwbpiXHbFmdYuu/77TKg2vRqs9g8eO2ALblGqGxcfVGm+TTu/CNDKJ8KV+I9DBJfhvv28oev/O7YFavvWQM36bLXaXZatjd+HS7L0wFNblZPlq8eTOqm42FuJt+7T+Jc3acSdlyr78AXYPbh9+/ZaLaJPnz664YYblJycrO+++06fffaZpk2bppkzZyopKemk9qNHj9Yjjzziup+Xl6eYmJharbE25eU6VF5mU1iYe1dOaNhRZWWdX0+4C50jzCmbt6WiTG+36UWZ3vKLcFa6zIaXQhR70xG1vL2iOzAsvlRlhTatHhumdsPyZPtl/Ia3rxTcrCKg6l+Sq8M/+CplXpCunJhdexsEl7xsn4rXcX33QReh9UuUddi30mWyD/sqtH7pSe1PFTQH9vorN8tH0TFF2vi91OGKbF3e9bD6/r6rio5UvKXPeCZeHX+XpR437a/0WprJqj16sLb4+fnpuuuu05gxY7Rq1SolJSVp3LhxlbZ1OBwKDg52u5msrMxLqVtD1aFzhmuazWYpoVOGNv9U+ZB3mMnbV6p/cYn2rz72pmQ5pQOrHVVefyo7anMF069sv2SeZZ3iwZySs8R2igaoSWVlXkpNCVKHK44dJNhslhKuyNbmjZW/R23eGKKEK9yvW3f8XVaV7SWpfsOjCgotdQWhw6/iYMc64ZjHsk5+3pwPztlNatu2rY4cqfxC8/no4w/i1POGHbo2cadimuXpwUc2yOFf7hpN+OhTa5Q09NgoQbvdqdi4HMXG5cju41T9iCLFxuWoUeNj18D8/MtcbSSpYaNCxcblKLJB5f3rqBtt7s3Xtg8ClfZxgHLS7PpufJjKirwUd2vF833F4+Fa93yIq32T7kXa+m6gtv/LX/m7vbVvpUMbXgpWk+5H5fVLeK17PkQH/+dQwR5vZW/x0brnQ3Tgvw616HXhvIbOBR/Pi1HPPvt07U37FdPiiB78y5aK1/HiitGEjz7zs5KGp7na/3N+jDpflaVbBuxSk+ZH1H9YulpenK8l7zWRVPEaHvRIquLb56pBdJE6XJGlsS/9oP27/LV2ZX1J0uaNwSrI89Gjz6SoRat812e2GjYu0v++rV/3f4Ra5vEPcWRmZur222/XoEGD1L59ewUFBWnNmjWaNm2aevfu7eny6sy3XzdRcGix7hmUorDwYqWnhmjsY1cpJ9tPkhTZoOiXi64VwiOK9MrbX7vu33Znqm67M1Wb1kfoyT93kSS1jM/W1JdWuNrc99APkqRlnzXVC1M618VmoRIt/lik4qwcbZgeUvHh4jYlunZmhvx/6R48st/b7Qi5/bA82WzShhdDVHjQW45wp2K6F6njyFxXm6OZXlrxRLiKDnnLN8ip0PhS9Xg7Q9FXM3qwLn37RUMFh5XqngfSFRZRovQtQRo7rINysirOiiKjjsp53BlRysYQTXvyYg14OF1Jw9O0d1eAJo1op52pFUPXnU6bWrQsUI+b9qteUJmyDjm0bnW43nklVmWlFU+SvBxfjR3WQQMeTtfkmetlt1vamVZPk0a01/at59/oQZtlnbKDodYVFxdr/Pjx+vLLL5WWlqbS0lLFxMTo9ttv11NPPSV/f//TriMvL08hISG69qIRsntzDeh81//T5Z4uAXVo/tUJni4BtazMWaL/ZM5Wbm7uaS/3ePxMy+FwaPLkyZo8ebKnSwEAnOPO6ppWcnKy7r77bl155ZXau3evJOmdd97RihUrTrMkAABnr9qhtXDhQiUmJsrf31/r1693fcg3NzdXzz77bI0XCADAr6odWn/961/1+uuv66233pKPz7FPW1999dVat25djRYHAMDxqh1aW7ZsUdeuXU+aHhISopycnJqoCQCASlU7tKKiopSamnrS9BUrVrh+HBIAgNpQ7dAaOnSoRowYoe+//142m0379u3T/PnzNWrUKA0bNqw2agQAQNJZDHl/8skn5XQ6de2116qwsFBdu3aVw+HQqFGj9PDDD9dGjQAASDqL0LLZbHr66af12GOPKTU1VQUFBWrbtq0CAwNroz4AAFzO+sPFvr6+atu2bU3WAgDAKVU7tLp37y6brepvjv7qq69+U0EAAFSl2qGVkJDgdr+0tFQbNmzQjz/+qIEDB9ZUXQAAnKTaoVXVrxiPHz9eBQX8NDwAoPbU2O9p3X333Zo1a1ZNrQ4AgJPUWGitXr1afn5+NbU6AABOUu3uwVtvvdXtvmVZ2r9/v9asWaMxY8bUWGEAAJyo2qEVEhLidt/Ly0vx8fGaOHGirr/++horDACAE1UrtMrLy3XvvfeqXbt2CgsLq62aAACoVLWuaXl7e+v666/n29wBAB5R7YEYl1xyidLT02ujFgAATumsfgRy1KhR+vTTT7V//37l5eW53QAAqC1nfE1r4sSJevTRR/XHP/5RknTTTTe5fZ2TZVmy2WwqLy+v+SoBAFA1QmvChAm6//779fXXX9dmPQAAVOmMQ8uyLElSt27daq0YAABOpVrXtE717e4AANS2an1Oq1WrVqcNrqysrN9UEAAAValWaE2YMOGkb8QAAKCuVCu0+vXrpwYNGtRWLQAAnNIZX9PiehYAwNPOOLR+HT0IAICnnHH3oNPprM06AAA4rRr7EUgAAGoboQUAMAahBQAwBqEFADAGoQUAMAahBQAwBqEFADAGoQUAMAahBQAwBqEFADAGoQUAMAahBQAwBqEFADAGoQUAMAahBQAwBqEFADAGoQUAMAahBQAwBqEFADAGoQUAMAahBQAwBqEFADAGoQUAMAahBQAwht3TBdQk5849ctp8PF0Gatk7F7fwdAmoQ//auczTJaCW5eU7FRF/Zm050wIAGIPQAgAYg9ACABiD0AIAGIPQAgAYg9ACABiD0AIAGIPQAgAYg9ACABiD0AIAGIPQAgAYg9ACABiD0AIAGIPQAgAYg9ACABiD0AIAGIPQAgAYg9ACABiD0AIAGIPQAgAYg9ACABiD0AIAGIPQAgAYg9ACABiD0AIAGIPQAgAYg9ACABiD0AIAGIPQAgAYg9ACABiD0AIAGIPQAgAYg9ACABiD0AIAGIPQAgAYg9ACABiD0AIAGIPQAgAYg9ACABiD0AIAGIPQAgAYg9ACABiD0AIAGIPQAgAYg9ACABiD0AIAGIPQAgAYg9ACABiD0AIAGIPQAgAYg9ACABiD0AIAGIPQAgAYg9ACABiD0AIAGIPQAgAYw+7pAnBMrwGHdNufDiosslTpKf6aMbaptm6sV2X7Ljdka8Cje9WwSYn27nBo1uQm+t/XIZIkb7ulgY/t1WXdc9WoaYmO5Htr/YogzZrSWFkHfetqk1AF9vWFY8mcCC18raGyM3zUom2Rhk3arfiOhZW2LSuVPnglSv/+sL4yD/ioSexR3fv0Pl3aPc+t3eH9Ppr9bGOt+SpYxUe91Kh5sUb+fadadah8vecTzrTOEV17ZWnomD36x4uN9NANbZSeEqBn/rFNIfVLK23fpnOBnnw5XV+8H6EH/9hGq78I1di30tSsVZEkyeHvVNwlhVowvZEe+mMbTbovVk1ij2r822l1uVmoBPv6wrH8n2F6a0IT3fXIfr38+WbFti3SmP5xyjlc+fnCvGnR+uwfERo2abde//pn/fGew/rrkFil/ejvapOf461RN7eSt93SxH+k6vWvf9bQsXsUFFJWV5vlUR4NrV69eqlnz56VzktOTpbNZtOmTZvquCrPuHXIQX3+boSWfRihXdv89fLopiou8lLiHZmVtr950CGtWR6ij96I0u5Uf817vrFSfwzQTUmHJEmF+d56qn8rJX8arj3pftq8PlAzxjRVq/aFiowuqctNwwnY1xeOj99qoJ53Hdb1d2SpaaujemjKLjn8nfryvfqVtv9qYbj6PnxAl12bp0bNSnTDwMO69A+5WvRGQ1ebj2Y0VGR0qR55YafiOxYqqmmJOnXLV6PmF8a+9mhoDR48WMuWLdOePXtOmjd79mxdeumlat++vQcqq1t2H6dativU+hXBrmmWZdP6FUFq06mg0mXadCrQ+hVBbtPWfhusNp2OVPk49YLL5XRKR/K8a6ZwVBv7+sJRWmJT6qYAJXTJd03z8pISfp+vzWsr7wouLfaSr8Nym+bws/TTf4+1/+7LELVsf0TP3tdCd7Zvp4eub63P51cegucjj4bWjTfeqMjISM2ZM8dtekFBgT788EMNHjy40uWKi4uVl5fndjNZcHiZvO06qcsg57CPwiIr7zIKiyxTToaPe/sMe5XtfRxODRq9V9/8M1yFBbyReQr7+sKRl2WXs9ymsAj3brvQyDJlnbA/f9Xpmjx9/GYD7U13yOmU1n0bpFVLQ5V16Fj7A7sc+tc7kYpuUay/LkjVDQMy9PrYGP37g/Ba3Z5zhUdDy263a8CAAZozZ44s69jRxYcffqjy8nLdeeedlS43efJkhYSEuG4xMTF1VbKRvO2Wnp6RLpssvfJ0U0+Xg1rEvjbb/RP3KLpFsf7Ura1uat5Rrz0dox53ZMrruHdqyynFXVKopNH7dNElRfq/uzPV867DWvpOhOcKr0MeH4gxaNAgpaWlafny5a5ps2fPVp8+fRQSElLpMqNHj1Zubq7rtnv37roqt1bkZdlVXiaFnnhEFlGq7CqOyLIz7Ao94Ug7NLLspPbedktPzUhXg8YlGt2/FUfeHsa+vnAEh5fJy9tS9oln1Rl2hVdxlhxSv0xjZ6Vr0bYNmvP9j3rz25/lX69cUU2LXW3CGpQqptVRt+Vi4o4qY9+FMVLU46HVunVrXXXVVZo1a5YkKTU1VcnJyVV2DUqSw+FQcHCw281kZaVe2vZDgBKuPtbNabNZSrg6XynrAitdJmVdoBKuzneb1un3eUpZd6zv+9c3scYtjmr0XS2Vn8MnHDyNfX3h8PG1FNe+UBuPux7pdEobVgSpdeeqr0dKkq+fpYhGpSovk1YuDdXvrs91zWt72RHtTfNza7833aEGjRmIUWcGDx6shQsXKj8/X7Nnz9ZFF12kbt26ebqsOrVoZkP9352H1eO2TMXEFenhZ3fJL8CpLz+ouMA66oXtuveJva72i2c10KXdcnXr0INqctFR3T1yn1q2L9QncxpIqngT+8vraWrV/oimDm8hL28pLLJUYZGlsvs4PbKNqMC+vnDcMvSQPl8QoX9/EK5d2/z06pMxKi7y0nW/jBT92/Bmmj052tV+87oArVwaqv07ffXj9/U0pn+cLKdNtz1w0G2dm9fV0/vTG2rfdoe+/jhMn82P0I1JGXW+fZ5wThyO9e3bVyNGjNCCBQs0b948DRs2TDabzdNl1alvl4QrJLxM9zyyr+IDpz/76y/3tFTO4YouoAbRJbKcx/4mKWsDNXV4rAaO2qukx/dq3w6HJg69SDu3VnyeIyKqRFf+cnT22hcpbo/1eN9W2vSd+2g01B329YWjW+9s5WXZ9c7fGik7w0exFxdp4j9SFRZZ0T2csc/X7XpVabGX5k1rpAO7HPIPcOrSP+Rq1PSdCgwpd7VplVCov8xM05wpjbXgxUaKiinRnybsUfdbs+t68zzCZh0/AsKDhgwZokWLFikvL0+7du1SdHT06Rf6RV5enkJCQtTd3kd2W+XXBQCY6V87/+vpElDL8vKdiojfodzc3NNe7jknugelii7C7OxsJSYmViuwAAAXjnOie1CSrrzySp0jJ30AgHPUOXOmBQDA6RBaAABjEFoAAGMQWgAAYxBaAABjEFoAAGMQWgAAYxBaAABjEFoAAGMQWgAAYxBaAABjEFoAAGMQWgAAYxBaAABjEFoAAGMQWgAAYxBaAABjEFoAAGMQWgAAYxBaAABjEFoAAGMQWgAAYxBaAABjEFoAAGMQWgAAYxBaAABjEFoAAGMQWgAAYxBaAABjEFoAAGMQWgAAYxBaAABjEFoAAGMQWgAAYxBaAABjEFoAAGMQWgAAYxBaAABjEFoAAGMQWgAAYxBaAABjEFoAAGMQWgAAYxBaAABjEFoAAGMQWgAAYxBaAABjEFoAAGMQWgAAYxBaAABjEFoAAGMQWgAAYxBaAABj2D1dQE2wLEuSVGaVergSADUtL9/p6RJQy/ILKvbxr+/lp3JehFZ+fr4kKbn8Ew9XAqCmRcR7ugLUlfz8fIWEhJyyjc06k2g7xzmdTu3bt09BQUGy2WyeLqdO5OXlKSYmRrt371ZwcLCny0EtYl9fWC7E/W1ZlvLz8xUdHS0vr1NftTovzrS8vLzUpEkTT5fhEcHBwRfME/tCx76+sFxo+/t0Z1i/YiAGAMAYhBYAwBiElqEcDofGjRsnh8Ph6VJQy9jXFxb296mdFwMxAAAXBs60AADGILQAAMYgtAAAxiC0AADGILQMkpSUJJvNpilTprhNX7x48QXzTSAXmoyMDA0bNkxNmzaVw+FQVFSUEhMTtXLlSk+XhhrSq1cv9ezZs9J5ycnJstls2rRpUx1Xde4itAzj5+enqVOnKjs729OloA706dNH69ev19y5c7V161Z98sknuuaaa5SZmenp0lBDBg8erGXLlmnPnj0nzZs9e7YuvfRStW/f3gOVnZsILcP06NFDUVFRmjx5sqdLQS3LyclRcnKypk6dqu7du6tZs2a6/PLLNXr0aN10002eLg815MYbb1RkZKTmzJnjNr2goEAffvihBg8e7JnCzlGElmG8vb317LPP6uWXX670yAznj8DAQAUGBmrx4sUqLi72dDmoJXa7XQMGDNCcOXPcfprjww8/VHl5ue68804PVnfuIbQMdMsttyghIUHjxo3zdCmoRXa7XXPmzNHcuXMVGhqqq6++Wk899RTXN85DgwYNUlpampYvX+6aNnv2bPXp0+eMv0j2QkFoGWrq1KmaO3euUlJSPF0KalGfPn20b98+ffLJJ+rZs6e++eYbderU6aSuJJitdevWuuqqqzRr1ixJUmpqqpKTk+karAShZaiuXbsqMTFRo0eP9nQpqGV+fn667rrrNGbMGK1atUpJSUmcZZ+HBg8erIULFyo/P1+zZ8/WRRddpG7dunm6rHMOoWWwKVOmaMmSJVq9erWnS0Edatu2rY4cOeLpMlDD+vbtKy8vLy1YsEDz5s3ToEGD+ChLJc6LH4G8ULVr1079+/fX9OnTPV0KakFmZqZuv/12DRo0SO3bt1dQUJDWrFmjadOmqXfv3p4uDzUsMDBQd9xxh0aPHq28vDwlJSV5uqRzEmdahps4caKcTqeny0AtCAwM1BVXXKEXXnhBXbt21SWXXKIxY8Zo6NCheuWVVzxdHmrB4MGDlZ2drcTEREVHR3u6nHMSP00CADAGZ1oAAGMQWgAAYxBaAABjEFoAAGMQWgAAYxBaAABjEFoAAGMQWgAAYxBaQA1JSkrSzTff7Lp/zTXX6M9//nOd1/HNN9/IZrMpJyenyjY2m02LFy8+43WOHz9eCQkJv6muHTt2yGazacOGDb9pPbiwEVo4ryUlJclms8lms8nX11dxcXGaOHGiysrKav2xFy1apEmTJp1R2zMJGgB8YS4uAD179tTs2bNVXFyspUuX6sEHH5SPj0+lP+tSUlIiX1/fGnnc8PDwGlkPgGM408J5z+FwKCoqSs2aNdOwYcPUo0cPffLJJ5KOdek988wzio6OVnx8vCRp9+7d6tu3r0JDQxUeHq7evXtrx44drnWWl5frkUceUWhoqOrXr6/HH39cJ36N54ndg8XFxXriiScUExMjh8OhuLg4vf3229qxY4e6d+8uSQoLC5PNZnN9w7fT6dTkyZPVokUL+fv7q0OHDvroo4/cHmfp0qVq1aqV/P391b17d7c6z9QTTzyhVq1aKSAgQLGxsRozZoxKS0tPavfGG28oJiZGAQEB6tu3r3Jzc93mz5w5U23atJGfn59at26tGTNmVLsW4FQILVxw/P39VVJS4rr/n//8R1u2bNGyZcv06aefqrS0VImJiQoKClJycrJWrlypwMBA9ezZ07Xc888/rzlz5mjWrFlasWKFsrKy9PHHH5/ycQcMGKB3331X06dPV0pKit544w0FBgYqJiZGCxculCRt2bJF+/fv10svvSRJmjx5subNm6fXX39dP/30k0aOHKm7777b9bPsu3fv1q233qpevXppw4YNGjJkiJ588slq/02CgoI0Z84c/fzzz3rppZf01ltv6YUXXnBrk5qaqg8++EBLlizR559/rvXr1+uBBx5wzZ8/f77Gjh2rZ555RikpKXr22Wc1ZswYzZ07t9r1AFWygPPYwIEDrd69e1uWZVlOp9NatmyZ5XA4rFGjRrnmN2zY0CouLnYt884771jx8fGW0+l0TSsuLrb8/f2tL774wrIsy2rUqJE1bdo01/zS0lKrSZMmrseyLMvq1q2bNWLECMuyLGvLli2WJGvZsmWV1vn1119bkqzs7GzXtKNHj1oBAQHWqlWr3NoOHjzYuvPOOy3LsqzRo0dbbdu2dZv/xBNPnLSuE0myPv744yrnP/fcc1bnzp1d98eNG2d5e3tbe/bscU377LPPLC8vL2v//v2WZVnWRRddZC1YsMBtPZMmTbKuvPJKy7Isa/v27ZYka/369VU+LnA6XNPCee/TTz9VYGCgSktL5XQ6ddddd2n8+PGu+e3atXO7jrVx40alpqYqKCjIbT1Hjx5VWlqacnNztX//fl1xxRWueXa7XZdeeulJXYS/2rBhg7y9vav18+mpqakqLCzUdddd5za9pKREHTt2lCSlpKS41SFJV1555Rk/xq/ef/99TZ8+XWlpaSooKFBZWZmCg4Pd2jRt2lSNGzd2exyn06ktW7YoKChIaWlpGjx4sIYOHepqU1ZWppCQkGrXA1SF0MJ5r3v37nrttdfk6+ur6Oho2e3uT/t69eq53S8oKFDnzp01f/78k9YVGRl5VjX4+/tXe5mCggJJ0r/+9S+3sJAqrtPVlNWrV6t///6aMGGCEhMTFRISovfee0/PP/98tWt96623TgpRb2/vGqsVILRw3qtXr57i4uLOuH2nTp30/vvvq0GDBiedbfyqUaNG+v7779W1a1dJFWcUa9euVadOnSpt365dOzmdTi1fvlw9evQ4af6vZ3rl5eWuaW3btpXD4dCuXbuqPENr06aNa1DJr7777rvTb+RxVq1apWbNmunpp592Tdu5c+dJ7Xbt2qV9+/a5flH3u+++k5eXl+Lj49WwYUNFR0crPT1d/fv3r9bjA9XBQAzgBP3791dERIR69+6t5ORkbd++Xd98842GDx+uPXv2SJJGjBihKVOmaPHixdq8ebMeeOCBU37Gqnnz5ho4cKAGDRqkxYsXu9b5wQcfSJKaNWsmm82mTz/9VBkZGSooKFBQUJBGjRqlkSNHau7cuUpLS9O6dev08ssvuwY33H///dq2bZsee+wxbdmyRQsWLNCcOXOqtb0tW7bUrl279N577yktLU3Tp0+vdFCJn5+fBg4cqI0bNyo5OVnDhw9X3759FRUVJUmaMGGCJk+erOnTp2vr1q364YcfNHv2bP3973+vVj3AKXn6ohpQm44fiFGd+fv377cGDBhgRUREWA6Hw4qNjbWGDh1q5ebmWpZVMfBixIgRVnBwsBUaGmo98sgj1oABA6ociGFZllVUVGSNHDnSatSokeXr62vFxcVZs2bNcs2fOHGiFRUVZdlsNmvgwIGWZVUMHnnxxRet+Ph4y8fHx4qMjLQSExOt5cuXu5ZbsmSJFRcXZzkcDqtLly7WrFmzqj0Q47HHHrPq169vBQYGWnfccYf1wgsvWCEhIa7548aNszp06GDNmDHDio6Otvz8/KzbbrvNysrKclvv/PnzrYSEBMvX19cKCwuzunbtai1atMiyLAZioGbYLKuKK8cAAJxj6B4EABiD0AIAGIPQAgAYg9ACABiD0AIAGIPQAgAYg9ACABiD0AIAGIPQAgAYg9ACABiD0AIAGOP/AVoPwLdPGdtqAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = CNN2D(n_features=n_features)\n",
    "model.load_state_dict(torch.load(checkpoint_path))\n",
    "\n",
    "model.eval()\n",
    "all_preds = []\n",
    "all_targets = []\n",
    "with torch.no_grad():\n",
    "    for X, X_rr, X_feat, y in val_loader:\n",
    "        X, X_rr, X_feat, y = X.to(device), X_rr.to(device), X_feat.to(device), y.to(device)\n",
    "        \n",
    "        outputs = model(X, X_rr, X_feat)\n",
    "        loss = criterion(outputs, y)\n",
    "\n",
    "        preds = torch.argmax(outputs, dim=1)\n",
    "        all_preds.extend(preds.cpu().numpy())\n",
    "        all_targets.extend(y.cpu().numpy())\n",
    "\n",
    "conf_mat = confusion_matrix(all_targets, all_preds)\n",
    "cm_train_plot = ConfusionMatrixDisplay(conf_mat / np.expand_dims(conf_mat.sum(axis=1), axis=1), display_labels=[num_to_label[i] for i in range(len(allowed_labels))])\n",
    "cm_train_plot.plot(colorbar=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f13f706-30e0-46c2-8cc6-a4f84bbd2d2e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
